{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "import pickle\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os.path as op\n",
    "import pylab as pl\n",
    "\n",
    "from rdkit import Chem\n",
    "from rdkit.Chem import AllChem\n",
    "from rdkit.Chem import rdMolDescriptors"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "novel_df = pd.read_csv('smiles_with_predicted_properties.csv')\n",
    "ref_df = pd.read_csv('')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['SMILES_generated', 'mol', 'synthetic_accessibility_score',\n",
      "       'molecular_weight', 'logP', 'TPSA'],\n",
      "      dtype='object')\n",
      "Index(['SMILES_minor', 'mol', 'synthetic_accessibility_score',\n",
      "       'molecular_weight', 'logP', 'TPSA'],\n",
      "      dtype='object')\n",
      "Index(['with_errors', 'corrected', 'synthetic_accessibility_score',\n",
      "       'molecular_weight', 'logP', 'TPSA'],\n",
      "      dtype='object')\n",
      "5836\n",
      "189936\n",
      "12040\n"
     ]
    }
   ],
   "source": [
    "# novel_df = pd.read_pickle('backups/novel_df.pickle')\n",
    "# minor_class_df = pd.read_pickle('backups/minor_class_df.pickle')\n",
    "# corrected_df = pd.read_pickle('backups/smiles_corrected.pickle')\n",
    "\n",
    "# print(novel_df.columns)\n",
    "# print(minor_class_df.columns)\n",
    "# print(corrected_df.columns)\n",
    "# print(len(novel_df))\n",
    "# print(len(minor_class_df))\n",
    "# print(len(corrected_df))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>with_errors</th>\n",
       "      <th>corrected</th>\n",
       "      <th>synthetic_accessibility_score</th>\n",
       "      <th>molecular_weight</th>\n",
       "      <th>logP</th>\n",
       "      <th>TPSA</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Oc1cc(CN2CCC(C2Cc2cccnH]n2)ccc1Cl</td>\n",
       "      <td>Oc1cc(CN2CCCCCC2c2ccc[nH]2)ccc1Cl</td>\n",
       "      <td>2.857206</td>\n",
       "      <td>304.134241</td>\n",
       "      <td>4.49100</td>\n",
       "      <td>39.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CC(CCNC(=O)CSc1nc(cc(n1)C(F)(F)F)-c1ccc(OC)cc1</td>\n",
       "      <td>CCCCNCC(=O)CSc1nc(cc(n1)C(F)(F)F)-c1ccc(OC)cc1</td>\n",
       "      <td>2.468444</td>\n",
       "      <td>413.138483</td>\n",
       "      <td>4.22190</td>\n",
       "      <td>64.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>COc1ccc(ccc1)-c1ncccc(C)c(NC(=O)c2cccc3)cc2n1</td>\n",
       "      <td>COc1ccc(cc1)-c1nc2ccc(C)c(NC(=O)c3ccco3)cc2n1</td>\n",
       "      <td>2.100574</td>\n",
       "      <td>359.126991</td>\n",
       "      <td>4.41072</td>\n",
       "      <td>77.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>(CCOc1ccc(Br)cc1C(O)=O</td>\n",
       "      <td>CCOCc1ccc(F)cc1C(O)=O</td>\n",
       "      <td>1.890565</td>\n",
       "      <td>198.069222</td>\n",
       "      <td>2.06040</td>\n",
       "      <td>46.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>c(Cc1ccc(cc)c(n1nnn11-c1ccccc1</td>\n",
       "      <td>Cl.Cc1ccc(c(O)c1)-n1nnn1-c1ccccc1</td>\n",
       "      <td>2.737724</td>\n",
       "      <td>276.077789</td>\n",
       "      <td>2.49382</td>\n",
       "      <td>55.87</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                      with_errors  \\\n",
       "0               Oc1cc(CN2CCC(C2Cc2cccnH]n2)ccc1Cl   \n",
       "1  CC(CCNC(=O)CSc1nc(cc(n1)C(F)(F)F)-c1ccc(OC)cc1   \n",
       "2   COc1ccc(ccc1)-c1ncccc(C)c(NC(=O)c2cccc3)cc2n1   \n",
       "3                          (CCOc1ccc(Br)cc1C(O)=O   \n",
       "4                  c(Cc1ccc(cc)c(n1nnn11-c1ccccc1   \n",
       "\n",
       "                                        corrected  \\\n",
       "0               Oc1cc(CN2CCCCCC2c2ccc[nH]2)ccc1Cl   \n",
       "1  CCCCNCC(=O)CSc1nc(cc(n1)C(F)(F)F)-c1ccc(OC)cc1   \n",
       "2   COc1ccc(cc1)-c1nc2ccc(C)c(NC(=O)c3ccco3)cc2n1   \n",
       "3                           CCOCc1ccc(F)cc1C(O)=O   \n",
       "4               Cl.Cc1ccc(c(O)c1)-n1nnn1-c1ccccc1   \n",
       "\n",
       "   synthetic_accessibility_score  molecular_weight     logP   TPSA  \n",
       "0                       2.857206        304.134241  4.49100  39.26  \n",
       "1                       2.468444        413.138483  4.22190  64.11  \n",
       "2                       2.100574        359.126991  4.41072  77.25  \n",
       "3                       1.890565        198.069222  2.06040  46.53  \n",
       "4                       2.737724        276.077789  2.49382  55.87  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corrected_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sanitize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sanitize(row):\n",
    "    m = Chem.MolFromSmiles(row)\n",
    "    if m:\n",
    "        try:\n",
    "            Chem.SanitizeMol(m)\n",
    "            return m\n",
    "        except Exception as e:\n",
    "            print(e)\n",
    "            return None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "novel_df['corrected_mol'] = novel_df.SMILES_generated.apply(sanitize)\n",
    "minor_class_df['corrected_mol'] = minor_class_df.SMILES_minor.apply(sanitize)\n",
    "corrected_df['corrected_mol'] = corrected_df.corrected.apply(sanitize)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "novel_df = novel_df.dropna()\n",
    "minor_class_df = minor_class_df.dropna()\n",
    "corrected_df = corrected_df.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(len(novel_df))\n",
    "print(len(minor_class_df))\n",
    "print(len(corrected_df))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ChEMBL search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'chembl_webresource_client'",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-22-93241d61db18>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mchembl_webresource_client\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'chembl_webresource_client'"
     ],
     "output_type": "error"
    }
   ],
   "source": [
    "import chembl_webresource_client"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'chembl_webresource_client'",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-21-42d896204008>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mchembl_webresource_client\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnew_client\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mnew_client\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0msimilarity\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnew_client\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msimilarity\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msimilarity\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfilter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msmiles\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"CO[C@@H](CCC#C\\C=C/CCCC(C)CCCCC=C)C(=O)[O-]\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msimilarity\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m85\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'chembl_webresource_client'"
     ],
     "output_type": "error"
    }
   ],
   "source": [
    "from chembl_webresource_client.new_client import new_client\n",
    "\n",
    "similarity = new_client.similarity\n",
    "res = similarity.filter(smiles=\"CO[C@@H](CCC#C\\C=C/CCCC(C)CCCCC=C)C(=O)[O-]\", similarity=85)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Molecular descriptors"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Synthetic accessibility score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "_fscores = None\n",
    "\n",
    "\n",
    "def readFragmentScores(name='fpscores'):\n",
    "    import gzip\n",
    "    global _fscores\n",
    "    # generate the full path filename:\n",
    "#     if name == \"fpscores\":\n",
    "#         name = op.join(op.dirname(__file__), name)\n",
    "    _fscores = pickle.load(gzip.open('%s.pkl.gz' % name))\n",
    "    outDict = {}\n",
    "    for i in _fscores:\n",
    "        for j in range(1, len(i)):\n",
    "            outDict[i[j]] = float(i[0])\n",
    "    _fscores = outDict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def numBridgeheadsAndSpiro(mol, ri=None):\n",
    "    nSpiro = rdMolDescriptors.CalcNumSpiroAtoms(mol)\n",
    "    nBridgehead = rdMolDescriptors.CalcNumBridgeheadAtoms(mol)\n",
    "    return nBridgehead, nSpiro\n",
    "\n",
    "\n",
    "def calculateScore(smiles_string):\n",
    "    if _fscores is None:\n",
    "        readFragmentScores()\n",
    "    \n",
    "    m = Chem.MolFromSmiles(smiles_string)\n",
    "    # fragment score\n",
    "    fp = rdMolDescriptors.GetMorganFingerprint(m, 2)  # <- 2 is the *radius* of the circular fingerprint\n",
    "    fps = fp.GetNonzeroElements()\n",
    "    score1 = 0.\n",
    "    nf = 0\n",
    "    for bitId, v in fps.items():\n",
    "        nf += v\n",
    "        sfp = bitId\n",
    "        score1 += _fscores.get(sfp, -4) * v\n",
    "    score1 /= nf\n",
    "\n",
    "    # features score\n",
    "    nAtoms = m.GetNumAtoms()\n",
    "    nChiralCenters = len(Chem.FindMolChiralCenters(m, includeUnassigned=True))\n",
    "    ri = m.GetRingInfo()\n",
    "    nBridgeheads, nSpiro = numBridgeheadsAndSpiro(m, ri)\n",
    "    nMacrocycles = 0\n",
    "    \n",
    "    for x in ri.AtomRings():\n",
    "        if len(x) > 8:\n",
    "            nMacrocycles += 1\n",
    "\n",
    "    sizePenalty = nAtoms**1.005 - nAtoms\n",
    "    stereoPenalty = math.log10(nChiralCenters + 1)\n",
    "    spiroPenalty = math.log10(nSpiro + 1)\n",
    "    bridgePenalty = math.log10(nBridgeheads + 1)\n",
    "    macrocyclePenalty = 0.\n",
    "    # ---------------------------------------\n",
    "    # This differs from the paper, which defines:\n",
    "    #  macrocyclePenalty = math.log10(nMacrocycles+1)\n",
    "    # This form generates better results when 2 or more macrocycles are present\n",
    "    if nMacrocycles > 0:\n",
    "        macrocyclePenalty = math.log10(2)\n",
    "\n",
    "    score2 = 0. - sizePenalty - stereoPenalty - spiroPenalty - bridgePenalty - macrocyclePenalty\n",
    "\n",
    "    # correction for the fingerprint density\n",
    "    # not in the original publication, added in version 1.1\n",
    "    # to make highly symmetrical molecules easier to synthetise\n",
    "    score3 = 0.\n",
    "    if nAtoms > len(fps):\n",
    "        score3 = math.log(float(nAtoms) / len(fps)) * .5\n",
    "\n",
    "    sascore = score1 + score2 + score3\n",
    "\n",
    "    # need to transform \"raw\" value into scale between 1 and 10\n",
    "    min = -4.0\n",
    "    max = 2.5\n",
    "    sascore = 11. - (sascore - min + 1) / (max - min) * 9.\n",
    "    # smooth the 10-end\n",
    "    if sascore > 8.:\n",
    "        sascore = 8. + math.log(sascore + 1. - 9.)\n",
    "    if sascore > 10.:\n",
    "        sascore = 10.0\n",
    "    elif sascore < 1.:\n",
    "        sascore = 1.0\n",
    "\n",
    "    return sascore"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "corrected_df['synthetic_accessibility_score'] = corrected_df.corrected.apply(calculateScore)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Molecular weight"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from rdkit.Chem.Descriptors import ExactMolWt\n",
    "\n",
    "def get_mol_weight_from_smiles(row):\n",
    "    m = Chem.MolFromSmiles(row)\n",
    "    if m:\n",
    "        try:\n",
    "            return ExactMolWt(Chem.AddHs(m))\n",
    "        except Exception as e:\n",
    "            print('row', row, e)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "row Cc1ccc(cc1)-c1nn(CCC(=O)NCc2ccncc2)(=O)c2ccccc12 Sanitization error: Explicit valence for atom # 9 N, 5, is greater than permitted\n",
      "row CC(C)c1ccc(cc1)-c1csc2ncn(-c3ccccc3)(=O)c12 Sanitization error: Explicit valence for atom # 15 N, 5, is greater than permitted\n",
      "row CSCCC(NCn1ccc2ccccc12)CC(=O)Cc1nn(C(C)CC)(=O)c2ccccc12 Sanitization error: Explicit valence for atom # 22 N, 5, is greater than permitted\n"
     ]
    }
   ],
   "source": [
    "corrected_df['molecular_weight'] = corrected_df.corrected.apply(get_mol_weight_from_smiles)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# logP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from rdkit.Chem.Crippen import MolLogP\n",
    "\n",
    "def get_logp_from_smiles(row):\n",
    "    m = Chem.MolFromSmiles(row)\n",
    "    if m:\n",
    "        try:\n",
    "            return MolLogP(Chem.AddHs(m))\n",
    "        except Exception as e:\n",
    "            print('row', row, e)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "row Cc1ccc(cc1)-c1nn(CCC(=O)NCc2ccncc2)(=O)c2ccccc12 Sanitization error: Explicit valence for atom # 9 N, 5, is greater than permitted\n",
      "row CC(C)c1ccc(cc1)-c1csc2ncn(-c3ccccc3)(=O)c12 Sanitization error: Explicit valence for atom # 15 N, 5, is greater than permitted\n",
      "row CSCCC(NCn1ccc2ccccc12)CC(=O)Cc1nn(C(C)CC)(=O)c2ccccc12 Sanitization error: Explicit valence for atom # 22 N, 5, is greater than permitted\n"
     ]
    }
   ],
   "source": [
    "corrected_df['logP'] = corrected_df.corrected.apply(get_logp_from_smiles)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# TPSA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from rdkit.Chem.rdMolDescriptors import CalcTPSA\n",
    "\n",
    "def get_tpsa_from_smiles(row):\n",
    "    m = Chem.MolFromSmiles(row)\n",
    "    if m:\n",
    "        try:\n",
    "            return CalcTPSA(Chem.AddHs(m))\n",
    "        except Exception as e:\n",
    "            print('row', row, e)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "row Cc1ccc(cc1)-c1nn(CCC(=O)NCc2ccncc2)(=O)c2ccccc12 Sanitization error: Explicit valence for atom # 9 N, 5, is greater than permitted\n",
      "row CC(C)c1ccc(cc1)-c1csc2ncn(-c3ccccc3)(=O)c12 Sanitization error: Explicit valence for atom # 15 N, 5, is greater than permitted\n",
      "row CSCCC(NCn1ccc2ccccc12)CC(=O)Cc1nn(C(C)CC)(=O)c2ccccc12 Sanitization error: Explicit valence for atom # 22 N, 5, is greater than permitted\n"
     ]
    }
   ],
   "source": [
    "corrected_df['TPSA'] = corrected_df.corrected.apply(get_tpsa_from_smiles)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Save results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['with_errors', 'corrected', 'synthetic_accessibility_score',\n",
      "       'molecular_weight', 'logP', 'TPSA'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(corrected_df.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "corrected_df.to_pickle('backups/smiles_corrected.pickle')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plot results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x864 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "plt.rcParams['font.size'] = 18\n",
    "plt.rcParams['xtick.labelsize'] = 18\n",
    "plt.rcParams['ytick.labelsize'] = 18\n",
    "\n",
    "fig, ax = pyplot.subplots(nrows=2, ncols=2, figsize=(16,12))\n",
    "\n",
    "sns.distplot(novel_df.molecular_weight, hist = False, kde = True, kde_kws = {'linewidth': 3}, color='red', label=\"generated\", ax=ax[0,0])\n",
    "sns.distplot(minor_class_df.molecular_weight, hist = False, kde = True, kde_kws = {'linestyle':'dashed', 'linewidth': 3}, label=\"source\", color='green', ax=ax[0,0])\n",
    "sns.distplot(corrected_df.molecular_weight, hist = False, kde = True, kde_kws = {'linestyle':'dotted', 'linewidth': 3}, label=\"corrected\", color='blue', ax=ax[0,0])\n",
    "ax[0,0].set(xlabel='Molecular weight', ylabel='Probability density')\n",
    "ax[0,0].set_xlim(50, 580)\n",
    "\n",
    "sns.distplot(novel_df.logP, hist = False, kde = True, kde_kws = {'linewidth': 3}, color='red', ax=ax[0,1])\n",
    "sns.distplot(minor_class_df.logP, hist = False, kde = True, kde_kws = {'linestyle':'dashed', 'linewidth': 3}, color='green', ax=ax[0,1])\n",
    "sns.distplot(corrected_df.logP, hist = False, kde = True, kde_kws = {'linestyle':'dotted', 'linewidth': 3}, color='blue', ax=ax[0,1])\n",
    "ax[0,1].set(xlabel='logP', ylabel='Probability density')\n",
    "ax[0,1].set_xlim(-2, 8)\n",
    "\n",
    "sns.distplot(novel_df.TPSA, hist = False, kde = True, kde_kws = {'linewidth': 3}, color='red', ax=ax[1,0])\n",
    "sns.distplot(minor_class_df.TPSA, hist = False, kde = True, kde_kws = {'linestyle':'dashed', 'linewidth': 3}, color='green', ax=ax[1,0])\n",
    "sns.distplot(corrected_df.TPSA, hist = False, kde = True, kde_kws = {'linestyle':'dotted', 'linewidth': 3}, color='blue', ax=ax[1,0])\n",
    "ax[1,0].set(xlabel='Topological polar surface area (TPSA)', ylabel='Probability density')\n",
    "ax[1,0].set_xlim(0, 140)\n",
    "ax[1,0].set_ylim(0, 0.02)\n",
    "ax[1,0].set_yticks([0, 0.005, 0.01, 0.015, 0.02])\n",
    "\n",
    "sns.distplot(novel_df.synthetic_accessibility_score, hist=False, kde = True, kde_kws = {'linewidth': 3}, color='red', ax=ax[1,1])\n",
    "sns.distplot(minor_class_df.synthetic_accessibility_score, hist=False, kde = True, kde_kws = {'linestyle':'dashed', 'linewidth': 3}, color='green', ax=ax[1,1])\n",
    "sns.distplot(corrected_df.synthetic_accessibility_score, hist=False, kde = True, kde_kws = {'linestyle':'dotted', 'linewidth': 3}, color='blue', ax=ax[1,1])\n",
    "ax[1,1].set(xlabel='Synthetic accessibility score', ylabel='Probability density')\n",
    "ax[1,1].set_xlim(1, 5)\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Probability density')"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(6,5))\n",
    "\n",
    "plt.rcParams['font.size'] = 14\n",
    "plt.rcParams['xtick.labelsize'] = 14\n",
    "plt.rcParams['ytick.labelsize'] = 14\n",
    "\n",
    "sns.distplot(novel_df.molecular_weight, hist = False, kde = True, kde_kws = {'linewidth': 3}, color='red', label=\"generated\")\n",
    "sns.distplot(minor_class_df.molecular_weight, hist=False, kde = True, kde_kws = {'linestyle':'dashed', 'linewidth': 3}, color='green', label=\"source\")\n",
    "sns.distplot(corrected_df.molecular_weight, hist = False, kde = True, kde_kws = {'linestyle':'dotted', 'linewidth': 3}, label=\"corrected\", color='blue')\n",
    "pl.xlim(50, 580)\n",
    "pl.xlabel('Molecular weight')\n",
    "pl.ylabel(\"Probability density\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Probability density')"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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7x+ZBjhyFvHkTtf3rLyheHIoV83eUyh9SUyBT58ko5S/G2BmL1xJMwYIwaRISko0OHQIbmjca39qYqmFVabz6CO3/PI+5dAmZODHJZaDT2QA5FUDaXaaUv3z5JUyf7tofOxZKlw5cPKkUGhzK1iFbGV3/NTruhCADfPGFTZ5KJUOTjFI36cs/v+S5355LudLy3397LJt86dGnoFMnP0TnA3372mJlAJs3w8qVgY1HpWuaZJS6CfEmng9Wf8DbK96mwsgKLNyzMHGjyEjo0cOWjwEiqzeg3IwPeeABWH3d9VvToQIFPB8effFFsk1Pn4bJk+Hll/0Ql0qXNMkodROW7l/K3rN7ASiQowB333p34kZPPumalZgrF991nsHJk8LUqfZ3dVycHwNOK0OGEB0MJ3NhV+2MiEjU5NIluOUW6N0bXn8dTp70f5gq8DTJKHUT3Gf496nRh5whOT0bTJ5sn71c89ln/H2iuHP3qacgONjXUaatP4/8SYftL1FoeBDP3QvExNjSywnkyeMaymwMLFjg3zhV+qBJRqkbdC7qHFO3T3XuD6ydoBjm3r12hclreveG/v0ZNco+onnsMRgwwE/BpqHY+Fhm75rN5WzxzK0ABmDUqCRvye6/3y7u+dpr0OA6ZdxU5qRJRqkbNGnzJKJiowCoXbw2tUvUdp10rHLJRcdiLOXL29FljlpmNWrY3TzXWQUgPbqz5J0UylkIgKP54K/i2GHZc+Ykavu//9nnTi++CBUq+DlQlS5oklHqBiUshulh0iT49Ve7HRRk9/Pl82N0vhMcFEzb29s69+deSx5ffZWobcDXw1EBp0lGqRuw6fgmNhzbAED24Oz0rtHbdfLMGY/hygwd6no4kUm0u72dc9uZZObPh8OHAxOQSrc0ySh1A8ZudD3M71q1KwVzFnSdHDYMTpyw2yVLwuuvs28f3HMPzJoF8fF+DtYH2tzexlkMdHVpOJMT+wcbNy7F92WGP7tKHU0ySqVSVGwUE/6e4Nx/uNbDrpMrV8KYMa79kSMhb14+/hiWLIGOHTNH+fsiuYrQsFRDAOIFFpR3nBg7NslM8sMPdoXpsDC7jI7KOjTJKJVKB88fpHgeOwy5bIGy3FP2HnsiJsazjleHDtCpE1FRMH6863D37n4M1ofaVXDrMqsWajf27YPFixO1HT3aJpozZ2yvmso6NMkolUoVC1dk65CtrHp4FZ/e9ylB4vhn9OGHsGWL3c6d297FiJAjh62+8uyz0KQJtG4duNjTknuSmV8piLhrD/mTmDPTztVUq9BkMVrqX0v9q7Rw8CBUrgxXrtj9Dz/0fPjvYEzmGXEVb+Ip+VFJjl86DsCaMdDgCJA9u+0TK1TI2faff+Cnn2yyqVkz8/wdZFWpKfWvdzJKpYW333YlmFq1bCmZJGSmX65BEsR9t99Hjmw57F1Nlcr2RHQ0TJzo0fb222H4cLsEQGb6O1DXp3cyeiejbtbhw3ay5dWrdn/RImjZMrAx+UnEpQjyZc9ny+mMGgX/+pc9UbMmbNqkGSWTSvM7GRHpJCIZrMKSUmnr0PlDtJnQhs/++IwD5w64Trz3nivBNGoE994LwOXLMG2as/hyplQsTzFXvbZevSCnY/vvv2H9+sAFptINb7vLJgJHRORdEanky4CUSq9m75rNwj0LGTpvKIN+GWQPHjtmh05d89JLzm/vM2dC1662EvHbbwcgYH/Lnx+6dXPtf/11ks1iY2Ht2sydfJWLt0mmOPAy0AzYJiIrRGSAiOT2XWhKpS+zd812bodXDLcb77/v+m1Zvz60aeNsc23Y8pkzWWgS4iOPuLYnT7Zr6bh54gkoXNgWzVyzxs+xqYDwKskYYy4aY74yxjQEagBrgbeBYyIyRkQa+jJIpQLt0tVL/L7vd+d+eMVwO6t/1ChXI7e7GGPsL9LbbrOnHnzQn9H6V1x8HDN3zGTInCE02PEM8RUddWYuXLBrzbiJjbWHAX7/HZUFpHp0mTFmGzACGA2EAj2A5SKyVkRqpnF8SqULi/Ys4mqcfe5Ss1hNbitwmx2mfG1EWe3a0L69s72IXQ1y717YsMGVbDIjEWHQL4P4ct2X/HH0DzYMcBXPTDhn5h7HvNVSpVyPb1Tm5nWSEZEQEekuIvOBfUAL4DGgGHAbsAuY4pMolQqwWbtmObfDK4bDqVPw+eeuBm53Me6Cgmz+ycyCJIg25V3dhPNr5nStxLZ8Oeza5TzXrh3s3m2nFQ0b5u9IVSB4O7psJHAM+BzYBtxhjGlijBlnjLlijDkKPA/ooACV6cTFxzFnl2utlPCK4TBihOt5Q82atoRMFuaeZBZErLKrlV3jdjeTN6+dM6Mjm7MOb+9kqgJPACWNMU87uswSOgrcc70LicgQEdknIlEisl5EklgU3dm2i4gsFJGTInLR0SWX6F+ziHQVkW0iEu34b2cv/1xKXdcfR/7g5GW7QH2x3MWon6+yLRlzzYsv2lsWh6w49ax1+dauqsyHVnN+QC/Xye++s3XdVJbkbZJ5FfjZGHPV/aCIZBORpgDGmFhjzNKULiIiPYBPgLeA2sAqYJ6I3JrMW5oBvwPtHe3nAtPdE5OINMJ2000Eajn++5OI6GKvKk24jyq7v+L9BP34k2vFy8qVoUsX5/mYGKhSBYYMsaOnskrCCcsdRp0SdQCIM3H8Vj4ISpSwJyMiklw1U2UN3iaZxUChJI7nd5zz1tPAOGPMGGPMdmPMUGw33L+SamyM+bcx5h1jzB/GmH+MMa8C64FObs3+Ayw2xrzpuOabwBLHcaVu2qydCZ7HuM//GDzY4y5mwQLYudMurfzAA1lo6DJ4rJY5f98iGDDAdTLBAIArV+C33+D55+HsWX9FqALB2yQjQFLfyQoDkUkcT3wBkVCgLrAwwamFwF1exgGQF3D/sWyUxDUXJHdNERkkIutEZN3JkydT8bEqKzp1+RQHzx8E7AqYLa8UtzMJAUJDE41Ndv/C/uCDruffWYHHw/9/5mPck8zcuXDkiHO3ZUv7eustWLbMn1Eqf0sxyYjILBGZhU0wE67tO15zgEXYLi9vFAGCgYgExyOwkz2vS0QeB0oB37sdLp6aaxpjRhtj6hlj6oWFhXnzsSoLK5KrCCefPcmCBxcwos0Ico+b5DrZpQsUKeLR/vPP7eJkDz8Mffv6N9ZAa1iqIfmy5wPg0IVD7Mgf4xqzHB9vn8043O32JFbny2Ru17uTOe14Cfbu4bTb6zAwCkjtNLOEd0TJ3SV5NhLpCrwP9DHGHEhw+oauqZQ3smfLTuvyrflXjQHwvdv3G/fZ7Q5BQdCsme0dqlrVj0GmAyHBIbQs5yoMOv+f+TBwoKvBN984+w9btLDPrh5/PMsPzMv0sqV00hgzAEBE9gMfGGO86hpLxikgjsR3GEVJfCfiwZFgvgf6GmNmJTh9/EauqVSqTZ/ueoBQtqzrW7pyalO+DdO2TwNg5+md0OUxKFAAzp2zM1OXLoV77qFVK9iW1BhVlel4W1bm1ZtMMDhGpq0HWiU41YoUutxEpDswAehvjPk5iSarU3tNpW7ImDGu7YEDPR74Kyu8YjjfdPiGw08dZtT9o+y0/j59XA0cgyZ0nkzWkex6MiLyN9DMGHNWRDaTQveTMcarcjKOIczfA0OAldiKAQOBasaYAyIy3nG9vo72PR3tn8GzmsBVY8wZR5u7gGXAi8B0oDPwGtDEGLM2pXh0PRmVkjHrx1A4V2Fal29NnoPHoYKjJldQEBw6ZMsrO8yeDWFh0KCB/gJNZNMmV9mD7Nlt5eqCBQMbk7opqVlPJqXusqnAtWLcSd1BpJoxZoqIFAZeAEoAW4B2bs9YEs6XecwR48eO1zVLgeaOa65yJKM3sPN59gA9rpdglEpJbHwsw34bxpkrZwgNDmXr5QHcfu1k+/YeCSY+3lYXPngQKla0CadixYCEnT7VqgV169r1ZaKjYcIEGDo00FEpP9GVMfVORiVh2YFlNBvXDICSeUty6P1Y5LjjMd/MmR5Pq5cscT2eKVjQflHPnt3PAad37qtmVq9uFzUTYd48mD/fjjCbMwduTW5atkpXfLEyZpCIBLntFxeRRxxdVUplOrN3us3yD63uSjAlStgqj26KFrVDlvPmtYtDaoKBeBPPhmMbeHv52xy+cBh694ZcuezJLVucc41GjIBPP7WHdChz5uTtk8s5wFAAEckDrMMOJ14qIllsNoDKCjwWKFvjNvd3wADI5tnLXLWqHZ17/Di88oqfAkznek3tRd3RdXnu9+eYu3su5MsHPXu6GjhWE23RwnXot9/8HKTyC2+TTF1sDTGALsAF7DDhR7EP5ZXKNHad3mWH3wI5s+WkxVS3teoffjjZ9+XKZR/+K2hY0rWO4YI9C+zGo4+6GkyZAhcuEB4OzzwDCxd6rmKtMg9vk0xe4JxjuzUw3RgTg0085X0RmFKB4t5V1jpbJXJGxdmdBg2gvP64e8O9jtmve38lJi7G/v3VqGEPXr4MkyZRrZpdwbpVK13ELLPyNskcBBqLSG6gDbacDNiimZd9EZhSgeLRVbbZrfB49+4e7a5ezTpVllOrcpHKlM5XGoAL0RdYc3iNHdvtfjejty5ZgrdJ5iPsfJXDwBHsvBSApsBmH8SlVECcvXKWFQdXOPfbz97hOtmtm0fbjz+GSpXgjTfstBnlIiIedzPOLrMHH4QcOez2xo12WLPK1Lyd8f8V0BB4GDvJ8VoB8z3YSZBKZQrz/plHnLHdY3eGlKH4BcePeuPGULq0s50xtt7j7t12zTIdGZWYR+n/f+bbjYIFPZO1WxUFY+xKzVeu+CtC5Q9e18Uwxqw3xkw3xlxyOzbHGLPSN6Ep5X8ea8fscpu636OHR7vDh113L7lyQdeu/oguY7m37L0Ei13rYP2x9ZyIPGFPuHeZTZwIly7x5pu2HFylSnbekco8UiyQ6c6x0uS92FFlHsnJGPNkGselVECMvG8k991+H7P//omOo+bagyKJskjp0nbI8syZdvJlnjwBCDady58jP41KN3J2Py7as4g+NftAkyZ2RdEdO+DSJZgyhTNnBnLAUfdj4UK4774ABq7SlLeTMZ/BFqLsj13iuIbbq7qvglPK38Jyh9GvVj9+vtCWGscdT/WbNvUoI3NNrlx28uXTT/s5yAzEfSEz53MZEc9lEsaMoXVru5knjw6myGy87S77N/CkMaaiMaa5MeYet1eL675bqYzmxx9d2wm6ypT3Ej78j7/2OLdfPwgJsdtr19I0bDvLlsGZM3ZAhco8vE0y+YC5vgxEqXTjyBFY4RhhFhSkD1xuQp0SdWh7e1tebf4qs3q6LQVVpIhH/becU8Zx992uvKMyD2+TzGSg7XVbKZVBHb14lNWHVhMXHwc//eTqs7nnHluczOHMGTtkef/+wMSZ0QRJEPP6zOOlZi/RoFQDgsTtV07//q7t77+H2Fi/x6d8z9skcwh4VUQmisj/icjT7i9fBqiUP3y78VvuGnsXJT4swRerPnGdSNBVNmWKHbJctqwt769uQps2UKyY3T52DH79NbDxKJ/wNsk8AlwC7sKu8TLU7aX/1FSG98PWHwA4efkkBXbstwezZYMuXTzaffeda7tyZT8Fl1mFhNjJmdeMG4cxsHkzfPihHXymMj5vJ2OWTeFVztdBKuVLW09sZcuJLQDkJIQOOx0nWraEwoWd7YyBf/8b2ra1k9bdiwor70RciuDQebfyCP36ubZnzODxR6KoWdMWzZw+3f/xqbSX6kXKRaSY+9oySmV0U7a6Vva+/1he8lwrV5agVpmIHbI8b56dI1OkiB+DzOAW7llI3dF1Kf5hcd5a/pbrRI0adtVMgOhoGsW75nYvXOjnIJVPeDtPJkRE3hORi9jaZWUcx98VkSE+jE8pnzLGeCSZHsvO2I1s2aBjx2Tflz+/ryPLXLIHZ2fDsQ2ALd3jsSKv2wCAln99ROHCNr8PGODnIJVPeHtH8jIQDjwIRLsd/wM7QVOpDGnT8U3sOr0LgDxkp91ux4kWLaBQocAFlsk0Kt2IvKF5AThw/gBbT251nezVyzl2ucTGuZxYup0pU6CvLoeYKXibZHoBjxljZgLxbse3ABXTPCql/MT9LqbTsXzkvDaKNsGh7afHAAAgAElEQVTcmHPnUDchNDjUY2Km+5o9FC7sMWcm6PvvUJmHt0nmFuBAEsezkYr6Z0qlJ8YYftjyg3O/x+KTdiMoCDp1ch6PjIRbb7VTZr79FuLi/B1p5hBeMdy5PWvXLM+TCefM6F9ypuFtktmKXTsmoe6ALgihMqS1R9Zy4Lz97lRActJ6j+NE06YeEzCnT4eLF2114PfftzlIpV67Cu2ckzHXHl5LxKUI10n3OTNHj8KiRc5TWsssY/P2n8urwEgReR4IBrqJyLfAMOB1XwWnlC9N2eLqKut6tACh1748J+gq27nTlVj69rWjzFTqFc5VmMalGwNgMMzZPcd1MsGcmYNf/MK778Kdd8I77/g7UpWWvJ0nMxt719Ia+0zmZaACEG6M0Wm6KkN6qtFTvN/qfeqG3UGP39y+VSeYgPn663b9mA8+8Jw7qFLPvcvMfZlrwONJ//IFlxk2DP78E6ZO9Vd0yhfEZOF70Xr16pl169YFOgwVaN98g3nkEQTgrrtgpa7D5ys7T+2k8ue2VEKukFyc/t9pcmRzLMdsDFSrBtu3c478FA0+TUxcMCEhtmZpWFgAA1ceRGS9MaaeN221d1mpn3/G2QOmFZd9qlKRSlQoVAGAyzGX+X2f27rVIs5acQU4z4sVf2T8eIiI0ASTkSWbZEQkXkTivHn5M2Cl0tTZs/Dbb659TTI+16FSB27JewuD6w6mVL5SnifdCpK+uO9hHup4gYIF/RygSlMpDT/uDlzrSysGvAZMx66QCdAI6IR9PqNUhrH79G7iTByVi1SG2bMhJsaeqFcPbrvN2e6HHyAqCjp31hn+aem1e17j/VbvI0mNoKhcGe64A/76y/7lz5qlD8IyuGTvZIwxPxtjphpjpmLXkhlujHnUGDPW8XoUeA5o769glUoLb694myqfV+GOUXfw68JRrhNudzHG2Af+AwbYkbXLlgUg0EwqV0iupBPMNe7LK0yZknw7lSF4+0ymBbA4ieOLgeZpFo1SPhYdG830Hba8798Rf5N3ldvAD7cks2ULbNtmt4ODXTUclR+4J5kFC+DsWSIiYM6c5N+i0i9vk8wp4IEkjj8AnEy7cJTyrYV7FnIuytaIKRMSxp37HF1lNWtChQrOdsWKwXvvQe3atuJJ7tyBiDZriI6N9jxQrhzUrw9AVEwQTRtEU6KE7bbU8j4Zj7dJ5iXgTRFZICKvOF7zgTfQZzIqA7m2OBlAj8MFXKPKHvD8DlW0KDz7LGzYAOPG+S28LGXcpnGETw6n0HuFOHLhiOdJx91MDqK5EnEeY+yjM72byXi8nYw5Hrsq5imgA9AROA00NsZoNTuVIVyOucysna6aWT1/2e86mWCZZXfZs/swqCxs4uaJ/LLrFy7HXOaXXb94nnRby6fLxfEEBRmaNUNHmmVAXs+TMcasNcb0McbUMcbUdmyv9WVwSqWlubvncunqJQAqhhTnjkOOrrJataCiFhP3txQLZpYuDY1tCZpBZhTH3hnPkiXQrp0fA1RpQidjqizDveJyzwN5XV1lbt+a4+Js1WXle+5J5re9vxF5NcFfvOPusjBnKDp3nB8jU2lJk4zKEi5GX/QoyNhj9j7XSbeusuXL7fOYXr08CgErHyhbsCzVi1YHIDoumkV7E/yFd+vmqky6dCkcO+bnCFVa8HuSEZEhIrJPRKJEZL2I3J1C2xIiMklEdjiqC4xLok1/ETFJvHL49A+iMpRZO2cRFRsFQI2QUlQ95lidrF49O5rJYfJkuHzZTsScMSMQkWYtHgUzdyYomFm8ODRrZreNgZ9/9mNkKq34NcmISA/gE+AtoDawCpgnIrcm85bs2MEG7wApPf+5DJRwfxljotIqbpXxua+A2XNPTteJBA/8r82NAXs3o3yrQyXXipi/7P6FeBPv2cCtK/P0j78xbhx07Ai/au33DMOrJCMinUQkOA0+72lgnDFmjDFmuzFmKHAM+FdSjY0x+40xTxpjxgFnUriuMcYcd3+lQawqE3mp2Us83fBpSuW5hR6z9rhOdOvm0W7ZMlvR5PXXbUFm5Vt3lryTorntAnEnIk/wx5E/PBt07OhcwOetFU0ZMMBWmvn+e39Hqm6Ut3cyE4EjIvKuiFS6kQ8SkVCgLrAwwamF2OHRNyOniBwQkcMi8ouI1L7J66lMpt4t9fiwzYcczPMS5U85vi03bOhRqwzs77OaNeGFF3QFTH8IkiDur3C/c999iDkAJUpAo0YA9Gai8/CMGRCdYA6nSp+8/WdUHDvpshmwTURWiMgAEUnNPOgi2FU1IxIcj3Bc/0btBB7Gzt3pBUQBK0WkQlKNRWSQiKwTkXUnT2qxgqxGfvrJtZPC3BjlP+GVUljIDJyLyNVhA52Lr+Kdd2zZH52/lDF4OxnzojHmK2NMQ6AG9vnI28AxERkjIg1T8ZkJV0mTJI55fzFjVhtjvjPGbDLGLAd6AHuAocm0H22MqWeMqRemi1RkLRERsNhRgk8kUVeZCoxW5VqRPTg7ObPlpFzBcs4BGk6dOwP2F8W0sy34vyEXKV3a/3GqG5PqDgFjzDZgBDAaCMX+Ul8uImtFpGYKbz0FxJH4rqUoie9ubpgxJg5Yh10eWmVx+87uc9YqY+pUiHd0lTVpAiVLOtt9/rmtxRgbG4Ags7jcoblZ2n8pp/93mpk9Z7pWyrymXDlb/h9sH9n8+f4PUt0wr5OMiISISHdHzbJ92MrMj2HXmrkN2AUkW5fbGHMVWA+0SnCqFXaUWZoQW0O8JnZAgcrinl74NGHvh9Hiuxasn/u164RbV9mFC/DMM9C2LdxyC2gvqv81KNWAnCE5k2/guJsBYNo03wek0oy3o8tGYn9pfw5sA+4wxjQxxowzxlwxxhwFngeuNyjgI6C/iDwiIlVE5BPgFmCU43PGi8j4BJ9dS0RqAfmAQo79qm7nXxaRNiJSztHuG2ySGYXK0qJio1i4ZyGx8bEs3r+YPGs32hNBQR5l/WfOtOtjgU0y2ouaDjmeywC2SmZ0NJGR9v+dSt9SWhnTXVXgCWCa444kKUeBe1K6iDFmiogUBl7AzmfZArQzxhxwNElqvszGBPvhwAGgjGO/ALbrrjhw3tG+qTEmwVhIldX8vu93LsdcBqCiFKHSqVP2RPPmdqKfQ7168NRTdn2snj0DEKi6vurVoXx52LMHLl5kcKcIJi6/lchI2LzZnlbpk7fdZa8CPydMMCKSTUSaAhhjYo0xS693IWPMF8aYMsaY7MaYusaYZW7nmhtjmidoL0m8yridf8oYc5vjekWNMW2MMatRWZ77DPLwnW5jSx56yKNdlSrw0Udw6BD8+9/+ik4ldCXmCr/s+oXBswfz3wX/9Twp4tFldm5nhLPGnM6ZSd+8TTKLgUJJHM9P0itmKhVQxhiP4bAdVp62GzlzenSVuQsKsqdVYOw4tYPwyeGM3jCabzd9S2x8glEYbl1mD53+GLBfENyqAql0yNskk9ww48KA1qxV6c7G4xs5ctEuhFXQZOeuQ44TnTpB3ryBC0wlq1bxWpTKVwqAs1FnWXlwpWeDBg3s5EygzYUfWTd6A1u3wuDB/o5UpUaKSUZEZonILGyCmXBt3/GaAywiDUeGKZVW3LvK2u8Wsl0ridW3r/N4dLRrRLMKPBHxXGMm4ez/oCD7JQEIIZa6W8dfqzij0rHr3cmcdrwEOOu2fxo4jB3B9aAvA1TqRrgvghX+l2PoWLFi0LKl8/iXX9q1sZ5+GrZu9XeEKinuSWbq9qkYk6ADxX0o8/TptjqzStdSHF1mjBkAICL7gQ+MMdo1ptK9wxcOs+HYBgBCTBBt/nHcrvTuDdlcP/I//ABHj8KIEbZvv1q1QESr3LUo24ICOQpwLuocB84fYNWhVTS+tbGrQfPmUKAAnDsHBw/Chg1Qt27A4lXX521ZmVc1waiMwn29+GYHIP+1Qopuo8rOnYO9e+12tmzJjgVQfpY9W3a6VXWV+5m0eZJng5AQCHfd7cRNncGvv0K/fh6rAqh0JNkkIyJ/i0hBx/Zmx36SL/+Fq9T1VQ2rSu8avSkQlIvw7Y67mGrVoFYtZ5sCBexdzPz58N57UCipsZMqIPrU6OPc/nHbj8TExXg2cBtldvin1bRqBePH26pBx3WRj3Qnpe6yqcC174C6JJ3KMJre1pSmtzUl5ssWxK53jLB/6CESPiXOlg3atLEvlX7cfdvdlM5XmkMXDnHq8ikW7llI+4rtXQ1at7Zjza9c4bZ/fqNpvcssW5eL+HiYPRsefTRwsavEkk0yxphXk9pWKkM4fJiQ35YQYrDJpU+f675FpQ9BEkSv6r14b9V7AEzaMskzyeTKBffd56xhNuT2hdS6qxMPPaSPZ9IjXZZJZU6TJrlGHrVoAaVKBTYelSq9a/QGQBAuXb2U4iizHnve4pNPbHkgHdKc/iR7JyMim/FynRdjTEol/pXyi3gTT7yJJ5sE2076a9we+BsD775rvwjXrKm/lNKrmsVq8nX417S9vS0l85VM3OD++21/Z2ws/PmnrQmki8ykSyk9k9HnMCpDWXt4Le0ntaddoQb0jNnK/WC7VtweFG/YAMOH21eDBrB6tSaa9EhEGFhnYPINChSwd6gLHau5z5gBQ5Ncp1AFmFfPZJTKCGbvms3ZqLNMPDqf7JXh/l3Y1S/dyshMnuxqX6mSJpgMrUsXV5KZPh2GDiUuDpYssfNutTJz+qDPZFSm4V4QM3yXY+Phhz3a3Hcf9Oplb3B69fJjcCrtdezo+pawdCmzJlzgtttsUYcPPwxsaMolpWcyfwPNjDFnr/d8Rp/JqEDbd3YfW05sASB7LLTag11/5O67Pdrde699RUZC9uwBCFSl2pYTW5j490T2ntvLlAfcFt8tXhwaNYJVqyA+nuI7l3LkiJ2o+fPPdkntXLkCFLRy0nkyKlNwv4u5dy/kjgEGDEi2Pyx3bj8Fpm7K2Stnqf1VbWfZ/7fvfZtyBd1q+3fpYpMMUH/jaCpWDOfMGbv43OXLmmTSA50nozKFRF1lIrbWiMrQCuYsSJvybZizew5gy8y80PQFV4POneGZZwCQXxcxZ80lbquWh5CQQESrkpKqZzIiUl5E7ne8yvsqKKVS43zUeZbsX+Lcv38Xdhq/29yYq8ktGq7SvWtzZgAmbp7oOWemXDm44w67HR3N7bvmaoJJZ7xKMiJSWERmALuBGY7XLhGZKSKFfRmgUtezYM8CZ3dKnaNQ6gK2q8zN4MFQp46tUxYREYAg1Q3rWKkjuUNs/+aOUzvYdHyTZ4OE5f9VuuLtnczXwO3A3UAOx6spUBYY45vQlPKO++JWHXZiq1127Og8Fh1tK5Bs3Aj/93+2QrzKOHKH5qZT5U7O/YmbJ3o2cJsHxZw59n+4gy5KF3jeJpk2wKPGmJXGmFjHayUw2HFOqYCIjY9l7u65zv3wXdg6ZW5DxzZtcv3eKV/elh9RGYt7ZebJWyYTFx/nOlm9uv0fC3DxIvz2G9u22Qm3ZcrAtm3+jVV58jbJnASSWk/mMnaVTKUC4mTkSe4oUo3geCh5AWofI9HcmAYNbBfZuHHw8ss6ATMjalmuJUVyFQHg6MWjLDuwzHVSxPNuZto0Xn4Z3nnHVpv5/ns/B6s8eJtkXgM+FhFnESHH9oeOc0oFRIm8JVgc04eT78G0KSC1anmsG3NN/vx2sJlbGTOVgYQEh9CjWg/nfqIuM/fnMjNn0reP605n/nxfR6dSktKiZZvdFiX7N1Af2C8i+x3LMe8HGgBP+iNQpZI1diwFo+DOIyS6i1GZh3uX2c/bfiYqNsp1skEDKFHCbp86Rdu8K+nbF2bOhLVr/Ryo8qAFMlXGtmWLrcILEBoKvXun3F5lWA1LNaRsgbLsO7ePW/LewsHzB6lYuKI9GRQEnTrBl18CEDJ7Gt991zSA0aprtECmyti+/da13akTFHaNqD961PbH9+3r+pKrMi4R4Yv2X1A8T3HuKHYHkvDhWpcuziTD9OkwYoQ+gEsHtECmyrD6T+vLs3u+ZNltEBtEorkx48fDsGF2mZEXXwxMjCpttb29LbWK10qcYACaNYOCBe32wYN2XQcVcN5OxgwVkVdFZJeIRIlInPvL10EqldCRC0f4fvNEPqh9heb94UzZ4tCqlfO8MTB2rN2Oi7Nl/VUmFxIC4eGufbeJmdu3wxid0RcQ3t7JvA70w44miweeBT7HDl8e4pvQlEreNxu/IR47067ZfijacyAEBzvPx8fDCy/YL7f583uOcFWZmPsos2nTuHoVGjaEqlVt1YdDhwIXWlblbZLpDjxmjPkKiANmGmOeBF4GWqX4TqXSWGx8LGP+HOXcH7we6N/fo01wsH0Ws2QJ7N2r1Xgzm4hLEXy69lMafdOIoxePuk60bu36n719O6F7d5Avn901BiZM8H+sWZ23SaYYcG3e7CWggGN7PtA6rYNSKiVzd8/lcOQxAMIioXPhJnD77cm2L1TIX5Epf+kzrQ//nv9v1hxeww9bfnCdyJUL2rZ17U+fTr9+duBh1672rkb5l7dJ5iBwi2P7H1ylZBoBV9I6KKVSMmqd6y7m4Y2QfcAjAYxGBUKv6q5lTcdtGudZmTlBwcyuXeHYMbuQ2T33+DFIBXifZKYD9zq2PwFeFZF9wDhs8Uyl/GL/uf3M/8c1hXvQ9lzwwAMebU6c8HdUyt+6VetGrhDbLbb5xGbWH1vvOnn//ZDNMTvjzz/JcfKQ3s0GkFdJxhgz3BjzpmP7Z2w15pFAF2PM8z6MTykPY9aPwThWAm/zD5S7r7fHMpc7d9o5MW3bwtSpgYpS+Vq+7PnoVrWbc3/sxrGukwUKQIsWrv0ZM/wYmUrohubJGGPWGGM+Msb8ktYBKZWcq3FX+WaD68Z58DoSzY0ZN86OLFuwQB/yZnYP13aVEJq0eRJXYtx67t2HE/70U6L3RkUlOqR8xOskIyJ1RGS8iKxzvL4XkTq+DE4pdzN3zCTisu0Lu+UC3E9FaNTIo83hw67tBPlHZTJ333o3txeyAz7OR59n2vZprpOdOrmGtC9fDgcOcO4cjBwJ9evb1SCUf3g7GbMP8CdQApjreBUD/hCRB1PzgSIyRET2OSZ1rheRu1NoW0JEJonIDsfEz3HJtOsqIttEJNrx385JtVMZW/4c+Wl4Pi8Aj2yAkAEDE5UN+f572LcP3noL7rsvEFEqfxERBtRyfZMYu8mty6xYMY/JuUyaREQEPPkkrFsHs2fDaV2kxC+8vZN5E3jRGNPKGPOS49UaeBF4w9sPE5Ee2IEDbwG1gVXAPBG5NZm3ZAdOAe8ASdZSFZFGwBRgIlDL8d+fRKSBt3GpjKG1KcfqERfZOAr+tSEo2br9ZcrYBat0rffMr98d/QgS+2vs932/s+/sPtfJB92+/37/PZUqGho4fiuIwB9/+DHQLMzbJBMG/JjE8Z+Aoqn4vKeBccaYMcaY7caYocAx4F9JNTbG7DfGPGmMGQecSeaa/wEWG2PedFzzTWCJ47jKTMaNA6DWcSjetJ1WvVSUzFeStre75sWM2zTOdbJTJ9egkO3bYdMmhg2zNTSPHdM7XX/xNsksBponcbw5sNSbC4hIKFAXWJjg1ELgLi/jSEqjJK654CavqdKbuDj47jvXvq4boxwermV/FormLkqe0DyuE7lze86ZmTCBTp3gscd0gq4/JVvqX0Tcqz3NA94WkXrAGsexhkAX4BUvP6sIEAxEJDgeAbT08hpJKZ7MNYsn1VhEBgGDAG69NbleOpWenIw8SeHl6wi69lQ/LAzat3eej421U2UeeAC6dYPs2QMUqAqI8ErhTO8xnfYV2hMSnKCP9MEHXcMMJ02C997zqHGnfC+lO5mf3V4jgcLYX85jHa9B2MQxMpWfaRLsSxLHUsvraxpjRhtj6hlj6oWFhd3kxyp/6PJjFyov686HjeB8duyzmNBQ5/lZs+wKiA89BPXq2RpVKusIDQ6lU+VOiRMMwL332kEAAMePw++/+zc4lXySMcYEefny9mvBKWxxzYR3GEVJfCeSGsd9cE2VTmw9sZUVB1ewO/slhrWEKyEkGpv81Veu7U6ddJ0q5SZbNujlKkHjPnnq1Ck7pPnawqrKN/y2aJkx5iqwnsRVm1thR5ndqNU+uKZKJ75a78ognXdA8Sr1oXp1jzYTJtghy2XL2nLuSsXExbh23EeZTZsGkZGMHm3HjTz5JHzxhf/jy0pSMxmzvYgsE5FTInJSRJaKSLtUft5HQH8ReUREqojIJ9jCm6McnzFeRMYn+NxaIlILyAcUcuxXdWvyCdBCRIaLSGURGQ7cA3ycythUOhN5NZLxf7l+HAavI8kH/mFhdsjynj1QqpQfA1TpSkxcDNO3Tyd8cjh3jXUb91OnDlSubLcvXYJZs6hd2z7LA1s4MzLS//FmFck++HcnIo8AX2DnoFwb4nM3MF1E/mWMGZvsm90YY6aISGHgBezEzi1AO2PMAUeTpJ7Eb0ywHw4cAMo4rrlKRHpi5+u8CuwBehhjkpxXozKOKVuncD76PAAVTsM9x7JDz57JttdusqwtMiaS3tN6ExVra8ZsPLaR2iVq2x+MBx+0q9gBTJhAvV96UbmyXa25b1/92fElMV48JRWR3cAnxpjPEhwfCgw1xlT0UXw+Va9ePbNu3bpAh6GSceeYO/nzqO0w/2AB/Ldsb5g4McBRqfTswWkPMnGz/Rl5ov4TjGznGJe0bx+UK2e3g4Ph6FEu5ixK3rwBCjSDE5H1xph63rT1trvsVuwCZQnNA27zNjClvLXh2AZngskeC/3+wqOrLD4evv0WLlwIUIAqXXIvmjlx80TnXQ1ly0KTJnY7Lg4mTdIE4yepWbQsqWWWW2O7rpRKU1+tcz3wf2AbFAm7zWPFqd9+szmnZEl47rlARKjSo+ZlmlO2QFkAzkadZeaOma6T7mWIRo3Sse5+4m2S+QD4RETGiMgAEekvIl8DIxznlEozF6IvOLs8AB679sA/yPXj+pmj4/bSJX1oq1yCJMijaOY3G79xnezVC+fty86d9puKm9OnNe/4greLln0F9ACqYJPKh0BloLsxZrTvwlNZ0cS/JxIZYzNHtRPQ+BCJ5sbcdx9UqWK3hwzxc4AqXetXqx+CfZL/695fOXDO0dmSNy/06+dq+PnnAPzyi51fVbw4rF+f8GrqZl03yYhINsdQ5WXGmCbGmMKOVxNjzMzrvV+p1AqvFM5L8XdzywV7FyNt74PSpT3aPPYYbN1qfylUqhSgQFW6dGv+W2lV3vbuGwzf/eVW8+7xx13bs2bBwYP8+KOtGBEb61keT6WN6yYZY0wsMA3Qx2TKL0rlKs6r3+zlwMfw6HrgkUeSbCdip0AoldDA2gOd299u+pZ4E293Kle2pWbAjh756iv693e97+BB/8WYVXj7TOYv4HZfBqKU04IFcOQI2eIhe+GicP/9gY5IZTAdK3WkUE5bann/uf0s3rfYddL9bmbMGJo3iubNN+1jmpnaN5PmvE0yrwAfikgnESktIoXcXz6MT2VFX3/t2u7Xz1kM8+pVOHQoQDGpDCV7tuz0qdGH0OBQelTrQeFchV0nw8Nd3a8nTxI09Seeew4qZsjZfumft5Mx49123d8ggElFkcx0RSdjpi8/bPmB2tlKUal6czuXAWDHDudDl+++sz1nvXvD//4H1aoFLlaV/h2/dJzQ4FDnHY2Ht96C55+32w0bwurV/g0ug0vNZExvk0xzUijHb4zxauGy9EaTTPpx+vJpSo8ozZXYK7TfBZOmQr4774ZlywA7tLRGDfuwH+zviOHDAxiwythOnLB3M1ev2v1166Bu3cDGlIGk+Yx/Y8wSY8zS5F43F65SMHr9aK7EXgHgSF7IG43HA//Tp6FIEbudJ48dXabUDSta1K5wd41jOPPVqzB1qq3mrXNm0kaKSUZEconI5yJyREROiMgkESnir+BU1nA17iqf/ekqi/fUGpB8+exSlw5FisCSJbB2rS3NXrBgAAJVGdrJyJOsOLjCdeCJJ1zbkycTf/I0lSvbH7vRo7UHLa1c707mVaA/MAf4AVta5ksfx6SymJ+2/sTRi0cBKH4Rem4B+vSBXLkStb3zTs/qIEpdz8Xoiwz7dRhlPylLt5+6cSXG3jHToIFrDHxUFEGffkyLFq73ffut/2PNjK6XZLoAA40xg4wxTwLtgU4ikiEf9Kv0xxjDiDUjnPuP/wmhcSQ7N0ap1AoNDmXC3xOIjInk+KXjroXwRODZZ10NP/mEAV0vcOut8OKLMGxYYOLNbK6XZEoDy6/tGGP+AGKxC40pddNWHFzB+mO2lkeOGEedsrp1nd8wrw0yU+pGZc+WneFNXKNE3l35rutupls3qOpYA/HiRe5a/i779sFrr0H58gEINhO6XpIJBq4mOBaLl4udKXU9H635yLnd9y8ochkYOtR5bOhQaNPG1jLUB7HqRg2sM5CSeUsCdmjz6PWOkovBwfDSS852MvJTgs6cCkSImVaKQ5gd82MWAdFuh+8DlgKXrx0wxnTwVYC+pEOYA2vPmT1UGFkB4xgdv+0zqEIRO+MyRw6OHIHbb4cox5Igq1ZBo0YBDFhlaJ//8TlPzLMP+4vnKc7eJ/eSMySnLS9Ts6ZrfPywYfD22wGMNP1LyyHM3wFHgdNurwnAoQTHlEq1T9d+6kwwbXdDlVPAoEGQIwcAK1e6usvq1bNz5pS6UQPrDOSWvLan3+NuJigIXn7Z1XDkSDhl72aiomDbNn9Hmrmk2O1ljBmQ0nmlbpQxhq0ntzr3n1qD7bpwmwDTvTvUr297M3QddnWzcmTLwfAmwxk6z3bHvrvyXQbVHWTvZrp2herVYcsWiIzk9Otf8FLsS0yaBAUKwJ49HssZqVTQvzYVECLCoocWsfJUR/6zGlrtwS7qkaCkf9my8P330CqpdVmVSqVH6jzivJs5dukYYzaMsScS3M3k/voTfpgczz2bmmkAACAASURBVLlzsH8//PprAILNJDTJqICRc+e4a+wiRiywRfDcH/gr5Qs5suVgWGPX2OR3VrxDVKzjoV+XLvbZDJDj8hn6llsJQLlycPlyokspL2mSUYHz7beuf701akDTphjjKiellC88WvdRSuQpAUDOkJzsPbvXnkhwN/PklkH8OjGC3bvtTba6MZpklN/Fm3g7osdRLwqwdzEizJlj15WaNMk2USqt5ciWg/dbvc/YDmPZ8fgOqoZVdZ3s1Alq1wagbPQO7p3+hD6LuUleVWHOrHQIs/+tObyGfjP68e/c99Jv8JfkjsE+WT18mLgcubnjDtdI0jffhOeeC2i4KitasQLuvtu1v2gRtGwZuHjSoTSvwqxUWhmxZgS7Tu/i8YNf8kxrx8GBAyF3bnbtguPH7aE8ebSyjAqQJk3gwQdd+088oX24N0Fn7iu/OXDuAD9v+9m5/9g67LBlx3K4VarYoaLvvw/58tlq7Er5gzGGiMgIiucpbg+8955di/niRczOnaz77w+Mie5LUBCMGhXYWDMavZNRfjNizQj7PAZosRfuiMBWWy5b1tkmf3544w278qVSvmaMYfG+xTT/rjl1R9d1jTQrUQJefRWA7VThzs/6MmYMjBsHZ84ELt6MSJOM8ovdp3fzxZ9fOPefXo2dXakPXVQAXY27Su9pvVl2YBlHLx7l6w1fu04+8QRUq0ZVtlOPPwGIjoYZMwIUbAalSUb5xTOLniEmPgaAxgeh3W6gRw9MxUrs3x/Q0FQWlj1bdo95M2+veNt1NxMS4hwBOZSR9GMcKz7dwACtg5IqmmSUz/2691dm7Zzl3P94vmPy5fPPM26cHbL81lsQExOoCFVWNqjuIOezmKMXjzJitWt9I5o1g1696Mv3jGMAjUf2RK7ozMzU0CSjfCo2PpanFjzl3O+/EeodBbp2JSKsOv/9r+2CeP55+PjjwMWpsq6cITl5semLzv03l7/JsYvHXA0++ADy5rXbu3drF28qaZJRPjVm/Ri2nNgCQO6r8NZvjhMvvMCFC66FocqWdQ4yU8rvBtUdRLWwagBExkTy3O9uieSWW2CE293NJ5/A4sV+jjDj0iSjfMYY41rqFnhuOZS4BISHQ61aVKgAq1fDRx/B6NGQK1fgYlVZW7agbIxo40ok4zaNY91Rt4naDz8M7dsDYIBFPb9hxiTtNvOGJhnlMyLCyodX8lL1x6ly0jGiDOwC6g7ZssFTT+mEahV4rcq3IrxiuHP/P/P/g7MiigiMGcOufPWoxSZan5jAfwZfJjY2QMFmIJpklE/lDs3Nq98f5u8vIUcsdi3l+vUDHZZSSfqw9YeEBIUAsPLQSn7c+qPrZIkSlB75P45ilwo4cKkIU1/YGIgwMxRNMsq3Fi6EmTPJ5ih2ubzzRyxcGNiQlEpOhcIVeLLBkwAESzD/nPnH43zOvt0YUnUpuYjkCUZy59jHdHbmdWiBTC2QmeYuRF8gX/Z8dkzyHXfA9u32eO/HqLHiSw4ehCFDbOWO3LkDHKxSCZyPOs9jcx7jxaYvelZodji35zTxjRpT6OROe6BzZ5g6NUst3ZquC2SKyBAR2SciUSKyXkTuvk77Zo52USKyV0QeS3D+FRExCV7HffunUMmJjY+l0TeN6DylM3s+fdWZYMiblxdyfsDBg3b3hx/gwoXAxalUcvLnyM/krpOTTDAABcoXptA377sOTJ8OX3yRZFvl5yQjIj2AT4C3gNrAKvj/9u47vopiC+D476TQOwIC0osUBSmKNCkqKv3BQxCBKIoCNkQURX2iKEXpCioWpGkEhFAEBZTeAkoIHUR6CwEkRJKQMu+PueQmIaHnbsg9388nH9jd2c25F5Jzd3bmDAtFpGQa7csAC1ztagBDgE9FpH2KpruAokm+7k6XF6Cu6MuNX7L95HaCdgZR5+Rgoi6WYP3f/3hncM7ExZ8+/9yWh1LqltSqlS07c1HfvhAS4lw8GZhHu8tEZD0QaozpkWTfHmCmMeatVNoPA9oZYyok2fc1UNUYU9e1PRD4rzHmrmuNR7vLbq7TUaep8GkFTkfZPurBS+CtVUDFirBlC2TJgjGwfDk0buxoqEpdk2PnjnE25iyVbqvk3hkdDXXrEhuylc1Up3aFCPjjD/fEzUwsQ3aXiUgWoBaQ8rHvIqBeGqfVTaX9r0BtEfFPsq+siBxxdcMFikjZy8TxnIhsFJGNJ0+evMZXoS7ng+UfJCaYMmfg1XWuA2PGQJYsgO221gSjbhXRcdEMWTmEip9VJCAoILGKOECcXzZGPLKIcvI3D7CCU3tOQa9e4MXPuVPjye6y2wBf4ESK/SeA29M45/Y02vu5rgewHngKeAzo4TpnjYgUTO2CxpgJxpjaxpjahQoVutbXoNKwM3wn4za4l1P+ZJEdsnyheVt49FEHI1Pq+h07d4yBywcSeSGS4CPBTAudlnjM1xem/FKIQ6YEUeTgC3rCtGl2PQCVyIkhzCnTvKSy70rtE/cbYxYaY6YbY0KNMUuAltjXFXAzglVX57VFrxGXYGemPbAf2u2Ag/7lKL9pOpMm6Yc7dWsqk78Mfe/vm7j95m9vEnkhErB35a+9ZvcXyhZBHlwjWV580b2GuPJokgkH4rn0rqUwl96tXHQ8jfZxwKnUTjDGRALbgAqpHVc337xd81iwZwEAYmyVZYPQvfgvHDrmz1NPQZ8+zsao1PUa0HBAsirNw1YNSzzWsSNMnAgHD/vyUmVXYb7z5+G//4XISCfCzXA8lmSMMReAP4CHUxx6GDt6LDVrgZQFRx4GNhpjUi0MLyLZgErAsdSOq5vr6LmjdJ/bPXG7+yaocRyO3/UQB3xt9UsfH3j8caciVOrG5M6am8FNByduD187nAP/HADso8annoJsBXPC9OmQPbtttHMn9Oiht/B4vrtsJPCUiDwrIpVFZAxQDPgCQEQmi8jkJO2/AO4QkdGu9s9in78Mv9hARIa75tKUEZE6wEwgJzDJQ6/Ja8UnxNNlVhfCz4cDUCwChiwBsmWj2PQxhGwWeveG/v2hfn1nY1XqRgTcE0CtorUAOxig3+J+XDIy96674Et3QVgCA+1YfS/n0SRjjPkR6AO8A4QADYDmxpgDriYlXV8X2+8DmgMPuNq/DbxsjPkpyWXvAH7AzpWZBcQA9ye5pkon8Sae6kWqA7abbNosKHQeW1a5cmVy5rQLC370kbNxKnWjfMSH0Y+6FzyauX1msoEuibp2ZW+nARxx1TejTx8IDvZQlBmTlpXReTI3JjycuS0rsNv3H/qtAVq2hLlzvarEhvIe3ed0Z2LIRMDWNvulyy88VNb26J8/D0OHwscfG9pkX8yP/zxiTypZEv78EwqmOuD1lpQh58moTCghAbp3p/V6m2BCCzQm5NVJmmBUpjWu+TjuLWariMebeJ6Z+wwX4i8ANo8MGgQxMcL0f5rxe07XsgEHD0K3bvbnxQtpklHXxBjj7ot+802YNw+AaLLyRK653PdoAYYMQdfZUJlSdv/sBHUKoljuYpTOV5qfO/9MFl870bhBA+jc2ba7917IP8g99JkFC2DYsFSumPlpd5l2l12TUWtHsfzAcr498wAFer+WuP/t+xYzONh2G+TIAVu32iWVlcqMtpzYwu25bqdQzuQTuo8ehYUL4emn7ahK3ngDPnEV0/TxgSVLoEkTzwd8k11Ld5kmGU0yV23j0Y3U+6YesQmxlDgLv02CCqeBNm04OOonOnb2Zd06O6CmZ88rXk6pzC82Fh58EFautNuFC8OmTVCsmLNx3SB9JqNuuoiYCDrN7ERsgp2eVCQSSp0FatWCadMoWcaX5cvtCM7nn3c2VqWcsPn4Zn7Y8kPynf7+dihz4cJ2OywMOnXyqv5kTTLqiowxPD//efae2QtA7hgInAlZipe0z2RcK49lyQLPPafP/ZX3CdoZRP1v69MtqBvL9y9Pdmz09GJ8/+zvrv4z7F3N2287EKUzNMmoK/pm0zcEbg1M3J4wD8rF5+Fs4EJdFEZ5vfiEeAatGMS/sf8SlxBH++nt2XdmH8bAO+/Aq69CwMdVWdjFXVyTjz+GOXOcC9qDNMmoy9oWto2XF7yUuP3sH9Bphy8zXlpB+dZVWJRyIQalvIyvjy+zO86mcE7bJXYq6hStA1tz7NQ5Zs+2beLiYPSxjphHH3OfGBAAf//tQMSepUlGpel01Gk6TmtLVHw0AFXDYMwSP3aPnM8zY6sTHm6r+E+f7nCgSjmsZN6SzO44O3E489awrfRc/CQLf4mnVClo3hxmBwkydYqdnAlw9ix06GAXP8vENMmoVP11+i/qjq/Jtoi/AMgeCz/O8SfHzDmcqfMouXLZdmXKQLNmDgaqVAZRr0Q9JrSckLg9b/c8Rm9/nRUrDEFBdmg/BQvCjBl2QADYGZyZvES5JhmVqolz3md3pLv82/jFWag65Vdo3pw6dewozBYtYOZMyJfPwUCVykAC7gng9XqvJ26PWjeKMTv64eeXZKrIffdhRozkDK4fnC+/hClTPByp52iSUckZA59+ygfPfU+rXZAtFqbPz8FTo5clm0RWpAjMnw81ajgXqlIZ0ZAHh9CucrvE7ZHrRtLnlz7JqjZ/leUFKmXdx3xa2B09e9oZzJmQJhnlFh0N3bvDyy/jG5fA9z/BqqD8tB23ioPF6zodnVK3BF8fXwLbB9K+cvvEfWODx7Js/zIA9uyBV/sKYTH5aMV8fuRx90Jn5845FHX60SSjuBB/gXGLPiThgYbJ1ifPVa021eeH0vmTGtx/v12HSSl1Zf6+/vzQ/gc6Vu0IwIhmI2hSxvYEhIVB3ry2XeWy0bTOvsRu7NoFTz4J8fFOhJxuNMl4udNRp3lk7L28uPZd+udLUmInIABWruT5QXcwcyYcOwaNGtk/lVJX5u/rz9R2U5nbaS5967qLZdavD1u22In/U2dkI/uEMe6T5s2Dvn1TudqtS5OMF9t7Ygd1h5ZnWUQoAMPrw9KyPjBmjF24PFs2AgISJ/TTuTPcfruDASt1i/Hz8aPVna0u2Z8jTxRTp8VTsybQpQu87h4s8PbYwgQGLMw0KzdrgUwvLZC5es2PtJ3fhfCs7hpKQ9blov/rc5CmTZO3XW3XIRs6VEvGKHWjzseep9UPrSiaqyjftf0OPx8/u9ZMx45snLmP+wjG4EODyuEsDL4tcbpARnItBTL90jsYlbEYY/hs/FO8dmIysVntvqxxMPnv6nSY+CuHY4tQIsU59evbL6XUjYmJi6FtYFt+3/c7AHEJcUz5zxT8ff1h8mQ+/G0D5oztYMq/az25dhS2i9PcwrS7zItEhB+h4xtleDl8MrG+dl+hf2FpnpdoOupPWvcoQp06cPKks3EqlVn5+/pTvkD5xO0ft/3IEz89QWx8LGTPzqQNVXgt79fkJJIRCX3scuY7djgY8Y3TJOMlQpcGUntIGWbkck+wrHkqK+tbzaHu62N5vJMP8+fbB/sBAV67UqxS6cpHfBjXfBwv3eeuB/jTjp94bNpjnIk6Q95ytzF8QyMO5a9OBf6yQ9EeeAA2bsQYeP992L3bwRdwHTTJZHYJCZjhw+k9rTN78sQm7u4ZUZHV7x+iTMPWAAwY4D6lUqVMN4pSqQxDRBjz6Bj61HGXk/lt32/U+boOu8J3QYUK5J832T3iJjwcmjRh9gdbGDgQqlWDwYO5ZQYGaJLJzA4cgGbNkNdfZ9IsQ95oyHkBphbuzecjdpEtv3vp2IcegpdftiMoR450l1ZSSt18IsLIR0YysNHAxH17Tu+hztd1WLR3kX0I+vvvUKAAALGR0bw60E6uiYmxP9q3yiAcTTKZkTHw9ddw993w228AlDsD0zdX5POy6xjx1ThCQy89bcwY2wWslEp/IsJ7jd9j+n+nk90vOwBnY87y2LTHGLt+LObee+0CZ8WL408cQbShFhu5Pe95hg1zOPhroEkmszlyhBlda/LV5z3cJSp8fKB/f7Y02ka3Z+uwaRP06qXPXZTKCDpU7cCq7qsonrs4AAkmgdk7ZxNv4qFKFVi1CsqXpwYhrKcOy8/eQ76+3SEyMvEaxiTbzFA0yWQWsbHsGP0OT/Yrw+MVQnihBQQXBypUsP9Jhw6l1X/88HMNWt+8GbZtczRipZRLzaI12dBjA3WK16Fs/rLM7DDTzp8BKF3a/gxXr44vCVRkj50sXauWXSoAW8i5WjVYv96515AWnYyZCSZjhs76nA9/7s/MEucwF/tpE4TGsaVY+s4210IWVr9+cPgwfPIJlEg5IUYp5ajouGiORx6ndL7Slx6MiLBdEN9/797n78/WV77i3s+6ER0t+PnBggXw8MPpG+e1TMbUO5lb2IbVM2jTpwjVt/RmRklXgon3g5Bu5P5yH+1KJE8wYJcWDwzUBKNURpTNL1uqCWbEmhH8fHwlTJ0KkyaRWAYgNpZDwwPJGmv7yqpUgYYNPRjwVdAkc6sxhlXzx/Poa0W4b8njzM0fluxw1T//B0GTOHeiFGNG5CAuLvnpPvovrtQtZdaOWfRb3I+WP7SkdWAbVjcuZ1cNrG1vJB7jF0Li7+ZBfiPwniFkiznrcMTJ6a+cW0VsLAQGsu3BajT84wV+zZM8ubQ7X5o/2y9i7Q/vkj+/3XfyJGzf7kCsSqmbIj4hnneXvpu4PW/3PBpMbED9FQHMmdifhDf7g68vpTnAEh6i8uQBcOed9o7H9Sjk99/trw+n6DOZDPxM5njEUWLWraJU0DK7Lnh4OACN2t/OishOEPokLR98haHP9qFqow6J540aZddAeuEFXRpZqVtd2L9h9Pq5F7N2zLrkWKXbKvF6qc48OXIJWZeuSH6wdm1WPzmeRv3u5e67YfhwePDBmxPTtTyT0SSTgZJMTFwMq/5exqLVk/h13xI2+57kmT/h67nJ2zXMMYdV5+1M/ffeg4EDPR+rUsqzdpzcwfA1w5kSOoXYhOS3JkVzFaVPtsb0+mQpuQ8cB+AseahGKAcpBdhFb7/55ubEoknmKjmdZMLPhxN6IpSQXcv5bfNslp3fznkfVz2X6NxwsAEFI305OXc+AlC8ODz3HDOKvcLjPezs37vusgsgKaW8w9FzRxmzbgxf/PEFETERifv9ffzZ98wWio/5FsaOJSE6hpH05V0GIRj2PPYKxQf1tEOfb5AmmavkVJLZuWMFTX9qw7H4f1JvcPg++GYNGF9yFdzEoY6fka9jADRoAD4+REVB+/bQrp1dFly7xJTyPmejzzLhjwmMWjeKY5HHePqep/m2zbf24OHD8MEHbJ33DftNUS6cuJd2BNljTZvCG29wqHIzXnhRaNsWWrWCQoXS/l4paZK5SumRZKLjogk5HkLwkWDWH1lPyLEQltYZT+E/d8HatbBmDef27SbPIz3gZBU4dSc80Qp87R1M+VPQ9Ehhvp59lARj6/EfOQLFit3UMJVSmURMXAzfb/meeiXqcedtdyY71vW7Nkw9MJfSZ+Dxbfar5jEQYFyxj3jxqK2M26wZ/Prr1X9PXbTMQxJMArtP7Wb94fUEHwkm+Egwm0+EEJsQB5u7wInqENGCX4c+TdfD+xLPyw34Ln2P+H9tGYn2a8vxUBGhWbX/ULZnV6hcmdB6QlSULVzpxZ8DlFJXkNUvK0/XePqS/dFx0cw5thSA/fnh4wb2q+xp6LAdFq92L4bWNvwrmFMYHnsMsmQB4N13ITjYzr3p2dMOWrsemmTS8NdfsHcvhJ+Oo8rdMdS4K2ey4zU6zWTLmhLER+WBZtOh4oLkF9jUHfY3AWB1pe/oijvJkCUL9bIeYaUryXTtEUqbx7MmO33lShJLwCil1LWKiImgfZX2zN4xm7NJ5s78XQCGNQAq94JdbfDf1pYfSw+k4xNHKZC9gC0X0KgRyxd2Y+UfOVm0CFq3vv4k4/HuMhHpDbwOFAW2AX2MMSsv074RMBKoChwFPjbGfHEj17woZ+57TKFiy2jYfhs1WwUTERPB6ajTHD53mFXjOxG23A4LbtrhK36rf95mnr/+gj17KBP/Nvv3uz49tHoWarmHbVQMhwtzprD/UBcAxuV7hd5NDkG9elC3LtSqxZQZ2Th0CCpXto9arqU/VCmlrtaF+Ass3ruYGdtnELQzKFnCuSjHBYgYAr5J0kEBOckZcxsAb/cKoGqVbBQuWZnCFe6hWpUmGbO7TEQ6AmOA3sAq158LRaSKMeZgKu3LAAuAb4EuQANgvIicNMb8dD3XTOp8pB8HdufjwKplTM36TvKDCTUT/xq27SDM+DDZ4ZJFz7Df9ffKh/LR+SzcdwRqxxehQNXaBN3vw7YG27ijVmEath0N5ZIv/tC16+UiU0qpmyOLbxZaVGxBi4otiImLYfHfi5m3ax4hJ0LYcmILUXFR3O1XDN8SfnDQ/so0wNvlG9CvRhU4WYWPCkyHU9FwCth0bd/fo3cyIrIeCDXG9Eiybw8w0xjzVirthwHtjDEVkuz7GqhqjKl7PddMfv3aBjZC7fHQ8oXkBzd3gc3dINs/1Mo/g42rZyQ7vD5bOY4Uu5s7y5fijjolyFuvKtSoAUWKXPX7oZRSTopPiGfvmb2cizlHrdtr2PLsK1bA8uUMiP2FIbWjUj9xIBlvdJmIZAHOA08YY2Yk2T8OuMsY0yiVc1YAW4wxLyTZ1wH4HsiBHSRxTddMKkeevKZunfIU8TtIYcLJGw15Y6B4BNwR6UOJLLdRNE9x/IsUhTvusGXzy5e3f5YtC9mz39ibopRSGVTosRDWbJhF2P5thJ06SFjkCcIunCFMzrPj04QM2V12G+ALnEix/wTwUBrn3A4sSaW9n+t6cq3XFJHngOdcmzG/L/lza+rfOgEIc31d4/3hrek2INzpIDIIfS/c9L1w0/fC7aqHATgxfinlrZOksu9K7S/ul8u0SfWaxpgJwAQAEdl4tdk4s9P3wk3fCzd9L9z0vXATkaueYOjJJBMOxGPvTpIqzKV3IhcdT6N9HPYRlFzHNZVSSnmIx0r9G2MuAH8AKddsexhYk8Zpa7m02+thYKMxJvY6r6mUUspDPN1dNhKYIiLBwGqgJ1AM+AJARCYDGGO6udp/AbwoIqOBL4H6wFPAE1d7zSuYcIOvJzPR98JN3ws3fS/c9L1wu+r3wqnJmG9gJ05uBV41xqxwHVsGYIxpnKR9I2AU7smYw9KYjJnqNZVSSjnHqwtkKqWUSl+6/LJSSql04/VJRkQKiMinIrJTRKJE5JCIfC4iBZ2OzVNEpLeI7BORaBH5Q0QaOh2Tp4nIWyKyQUQiROSkiMwTkbucjstpIjJARIyIfOZ0LE4RkaIiMsn1/yJaRLa7uvG9ioj4isigJL8r9onIhyJy2Wf7Xp9ksIMEimOf6dyNrZH2APCDk0F5SpLab4OBGthReQtFpKSjgXleY2A8UA9oih0mv0RECjgZlJNE5H6gBxDqdCxOEZF82AFFArQAKgMvYWdpe5v+wAvAy0Al4BXX9uXLd+kzmUuJSHNgPpDPGBNxpfa3shup/ZaZiUgu4CzQ1hgzz+l4PE1E8gJ/YpPM/4CtxpgXnY3K80RkMNDIGFPf6VicJiLzgVPGmIAk+yYBBY0xLdM6T+9kUpcHiMHWRcu0XPXkagGLUhxahP1E781yY38+zjgdiEMmYD9o/O50IA5rC6wXkR9FJExEQkTkRRGRK56Z+awCmohIJQARqYK9619wuZN0WawUXLfHg4CvjDFxTseTzq6nnpy3GAOEYCcEexUR6QGUB3RBCiiLXT5kFDAUuAf41HXM255TDcN++NouIvHY/PGRMWb85U7KtHcyrgdS5gpfjVOckxOYBxzBPqPxFtdaTy5TE5GR2LWL2htj4p2Ox5NE5E7s87knXRU1vJ0P8Kcx5i1jzCZjzERgLPZZhLfpCHQDOgM1XX/vLSLPXO6kzHwnMxqYeoU2iYuaufrgL972tTTGRKdXYBnI9dSTy9REZBTQCWhijPnb6XgcUBd7h7s1SY+QL/CAiPQEchpjYpwKzgHHgO0p9u3APvT2Np8Aw40xga7tLSJSCvvg/5u0Tsq0ScYYE85VluUWkdzAQuwn+EeNMZHpGVtGYYy5ICIXa78lXZXtYeAnZ6JyjoiMwSaYxsaYnU7H45AgIGWF3YnAHuwdjrfd3azm0rL2FYEDDsTitBzYD6VJxXOFHrFMm2SulivBLMI+7G8L5HR1mwGc9oIugxup/ZZpuBa664r9P3BGRC7e3UV6y4cOAGPMP8A/SfeJyL/Yn4U01l7K1EYBa0TkbeBH7DD/l4EBjkbljHnAmyKyD9iGfS/6ApMvd5LXD2F2PZdZmsbhJsaYZZ6Lxhla+w1EJK0fhPeNMQM9GUtG46op6JVDmAFEpAX2Lu5ObBf7Z8Cnxst+ebo+kA8C/oPtUj8GBAIfXO7xgtcnGaWUUukn044uU0op5TxNMkoppdKNJhmllFLpRpOMUkqpdKNJRimlVLrRJKOUUirdaJJRSimVbjTJKJXOROQ711ocSnkdTTJK3WJEpHGKauInRWShiFR3OjalUtIko9Stqyq2FFALID/wi2tFS6UyDE0ySnmQiGQVkdEickJEokVknYg0SNGmhYjsch1fISKdXHcspVNcLswYc9wYEwy8hl2y4X7PvBKlro4mGaU862Ps4k/dsVVst2DvQIoCiEhJYBbwM1Adu0DWx1dx3SjXn/43O2ClboQmGaU8xLWERC+gvzHmZ2PMDuzSCidwr7TYC/gbeM0Ys8sYM5MrLLsgIgWB94BzQHB6xa/U9fD69WSU8qBy2DuN1Rd3GGPiRWQtUMW1qxKwIUUZ+fVpXG+/a/XKnNhFxToYY8JuetRK3QBNMkp5zsX1jFNbX8MkaXO16280AU4DJ40xETcYm1LpQrvLlPKcv7DLFyc+6BcRX6Au7nXkdwD3pjjvvjSut88Ys1cTjMrINMko5SHGmH+Bz4GhItJcRCq7tosA413NvgDKichwzJUY6gAAAKlJREFUEblTRNoBz1+8hMeDVuoGaZJRyrP6A9OBiUAIUA141BhzDMAYcwBoD7QGNgOvAu+7zk1ziVulMipdflmpDE5EXgE+APIbYxKcjkepa6EP/pXKYETkBWADcBI7ufJd4DtNMOpWpElGqYynPDAAKAgcxj6n+cDRiJS6TtpdppRSKt3og3+llFLpRpOMUkqpdKNJRimlVLrRJKOUUirdaJJRSimVbv4PJmYZdo9DCrEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(6,5))\n",
    "\n",
    "plt.rcParams['font.size'] = 14\n",
    "plt.rcParams['xtick.labelsize'] = 14\n",
    "plt.rcParams['ytick.labelsize'] = 14\n",
    "\n",
    "sns.distplot(novel_df.logP, hist = False, kde = True, kde_kws = {'linewidth': 3}, color='red')\n",
    "sns.distplot(minor_class_df.logP, hist=False, kde = True, kde_kws = {'linestyle':'dashed', 'linewidth': 3}, color='green')\n",
    "sns.distplot(corrected_df.logP, hist = False, kde = True, kde_kws = {'linestyle':'dotted', 'linewidth': 3}, color='blue')\n",
    "pl.xlim(-2, 8)\n",
    "pl.xlabel('logP')\n",
    "pl.ylabel(\"Probability density\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Probability density')"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(6,5))\n",
    "\n",
    "plt.rcParams['font.size'] = 14\n",
    "plt.rcParams['xtick.labelsize'] = 14\n",
    "plt.rcParams['ytick.labelsize'] = 14\n",
    "\n",
    "sns.distplot(novel_df.TPSA, hist = False, kde = True, kde_kws = {'linewidth': 3}, color='red')\n",
    "sns.distplot(minor_class_df.TPSA, hist=False, kde = True, kde_kws = {'linestyle':'dashed', 'linewidth': 3}, color='green')\n",
    "sns.distplot(corrected_df.TPSA, hist = False, kde = True, kde_kws = {'linestyle':'dotted', 'linewidth': 3}, color='blue')\n",
    "pl.xlim(0, 140)\n",
    "pl.ylim(0, 0.02)\n",
    "pl.yticks([0, 0.005, 0.01, 0.015, 0.02])\n",
    "pl.xlabel('Topological polar surface area (TPSA)')\n",
    "pl.ylabel(\"Probability density\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SAS histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Probability density')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "fig = plt.figure(figsize=(6,5))\n",
    "\n",
    "plt.rcParams['font.size'] = 14\n",
    "plt.rcParams['xtick.labelsize'] = 14\n",
    "plt.rcParams['ytick.labelsize'] = 14\n",
    "\n",
    "sns.distplot(novel_df.synthetic_accessibility_score, hist = False, kde = True, kde_kws = {'linewidth': 3}, color='red', label=\"generated\")\n",
    "sns.distplot(minor_class_df.synthetic_accessibility_score, hist=False, kde = True, kde_kws = {'linestyle':'dashed', 'linewidth': 3}, color='green', label=\"source\")\n",
    "sns.distplot(corrected_df.synthetic_accessibility_score, hist = False, kde = True, kde_kws = {'linestyle':'dotted', 'linewidth': 3}, label=\"corrected\", color='blue')\n",
    "pl.xlim(1, 5)\n",
    "pl.xlabel(\"Synthetic accessibility score\")\n",
    "pl.ylabel(\"Probability density\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Probability density')"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.rcParams['figure.figsize'] = (6, 6)\n",
    "plt.rcParams['axes.titlesize'] = 16\n",
    "plt.rcParams['font.size'] = 16\n",
    "plt.rcParams['xtick.labelsize'] = 16\n",
    "plt.rcParams['ytick.labelsize'] = 16\n",
    "novel_df.synthetic_accessibility_score.hist(\n",
    "    bins=48, \n",
    "    align='mid',\n",
    "    density=True, \n",
    "    grid=False,\n",
    "    color='blue'\n",
    ")\n",
    "pl.xlim(1, 4.5)\n",
    "pl.title(\"Novel generated molecules (A)\")\n",
    "pl.xlabel(\"Synthetic accessibility score\")\n",
    "pl.ylabel(\"Probability density\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Probability density')"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "corrected_df.synthetic_accessibility_score.hist(\n",
    "    bins=48, \n",
    "    align='mid',\n",
    "    density=True, \n",
    "    grid=False,\n",
    "    color='red'\n",
    ")\n",
    "pl.xlim(1, 4.5)\n",
    "pl.title(\"Corrected molecules (B)\")\n",
    "pl.xlabel(\"Synthetic accessibility score\")\n",
    "pl.ylabel(\"Probability density\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "corrected mean 2.4880100336429836\n",
      "corrected median 2.3694462515439083\n",
      "corrected mean 2.625994009189228\n",
      "corrected median 2.4889649477990083\n",
      "generated mean 2.6291337992989314\n",
      "generated median 2.506272855358497\n"
     ]
    }
   ],
   "source": [
    "print('corrected mean', minor_class_df.synthetic_accessibility_score.mean())\n",
    "print('corrected median', minor_class_df.synthetic_accessibility_score.median())\n",
    "print('corrected mean', corrected_df.synthetic_accessibility_score.mean())\n",
    "print('corrected median', corrected_df.synthetic_accessibility_score.median())\n",
    "print('generated mean', novel_df.synthetic_accessibility_score.mean())\n",
    "print('generated median', novel_df.synthetic_accessibility_score.median())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Substructure Search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['with_errors', 'corrected', 'synthetic_accessibility_score',\n",
      "       'molecular_weight', 'logP', 'TPSA', 'corrected_mol'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(corrected_df.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "corrected_df['mol'] = corrected_df.corrected.apply(Chem.MolFromSmiles)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_ring_info(mol):\n",
    "#     mol = Chem.AddHs(mol)\n",
    "    rc = mol.GetRingInfo().NumRings()\n",
    "    nfrags = len(Chem.GetMolFrags(mol))\n",
    "    ec = mol.GetNumBonds() - mol.GetNumAtoms() + nfrags\n",
    "    return max(rc, ec)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "corrected_df['rings'] = corrected_df.mol.apply(get_ring_info)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.0598006644518274\n",
      "9.044850498338871\n",
      "24.559800664451828\n",
      "31.75249169435216\n",
      "26.27076411960133\n",
      "6.312292358803987\n"
     ]
    }
   ],
   "source": [
    "l = len(corrected_df)\n",
    "\n",
    "print(len(corrected_df[corrected_df.rings == 0]) / l * 100)\n",
    "print(len(corrected_df[corrected_df.rings == 1]) / l * 100)\n",
    "print(len(corrected_df[corrected_df.rings == 2]) / l * 100)\n",
    "print(len(corrected_df[corrected_df.rings == 3]) / l * 100)\n",
    "print(len(corrected_df[corrected_df.rings == 4]) / l * 100)\n",
    "print(len(corrected_df[corrected_df.rings > 4]) / l * 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "def contains_nitrogen(smiles):\n",
    "    smiles = smiles.lower()\n",
    "    if 'n' in smiles:\n",
    "        return 1\n",
    "    else:\n",
    "        return 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "corrected_df['contains_nitrogen'] = corrected_df.corrected.apply(contains_nitrogen)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "def contains_sulfur(smiles):\n",
    "    smiles = smiles.lower()\n",
    "    if 's' in smiles:\n",
    "        return 1\n",
    "    else:\n",
    "        return 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "corrected_df['contains_sulfur'] = corrected_df.corrected.apply(contains_sulfur)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "def contains_oxygen(smiles):\n",
    "    smiles = smiles.lower()\n",
    "    if 'o' in smiles:\n",
    "        return 1\n",
    "    else:\n",
    "        return 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "corrected_df['contains_oxygen'] = corrected_df.corrected.apply(contains_oxygen)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "halogens = ['f', 'cl', 'br', 'i', 'at']\n",
    "\n",
    "def contains_halogen(smiles):\n",
    "    smiles = smiles.lower()\n",
    "    if any(hal in smiles for hal in halogens):\n",
    "        return 1\n",
    "    else:\n",
    "        return 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "corrected_df['contains_halogen'] = corrected_df.corrected.apply(contains_halogen)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "91.7358803986711\n",
      "35.946843853820596\n",
      "91.51162790697674\n",
      "39.260797342192696\n",
      "0.9966777408637874\n"
     ]
    }
   ],
   "source": [
    "print(len(corrected_df[corrected_df.contains_nitrogen == 1]) / l * 100)\n",
    "print(len(corrected_df[corrected_df.contains_sulfur == 1]) / l * 100)\n",
    "print(len(corrected_df[corrected_df.contains_oxygen == 1]) / l * 100)\n",
    "print(len(corrected_df[corrected_df.contains_halogen == 1]) / l * 100)\n",
    "print(len(corrected_df[(corrected_df.contains_nitrogen == 0) & (corrected_df.contains_oxygen == 0) & (corrected_df.contains_sulfur == 0)]) / l * 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "from rdkit.Chem.rdMolDescriptors import CalcNumSpiroAtoms\n",
    "\n",
    "def get_number_of_spiro_atoms(smiles):\n",
    "    return CalcNumSpiroAtoms(Chem.MolFromSmiles(smiles))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "corrected_df['spiro_atoms'] = corrected_df.corrected.apply(get_number_of_spiro_atoms)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.935215946843854\n"
     ]
    }
   ],
   "source": [
    "print(len(corrected_df[corrected_df.spiro_atoms > 0]) / l * 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "from rdkit.Chem.rdMolDescriptors import CalcNumHeterocycles\n",
    "\n",
    "def get_number_of_heterocycles(smiles):\n",
    "    return CalcNumHeterocycles(Chem.MolFromSmiles(smiles))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "corrected_df['heterocycles'] = corrected_df.corrected.apply(get_number_of_heterocycles)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "73.62126245847176\n"
     ]
    }
   ],
   "source": [
    "print(len(corrected_df[corrected_df.heterocycles > 0]) / l * 100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Common functional groups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from rdkit import Chem\n",
    "from collections import namedtuple\n",
    "\n",
    "def merge(mol, marked, aset):\n",
    "    bset = set()\n",
    "    for idx in aset:\n",
    "        atom = mol.GetAtomWithIdx(idx)\n",
    "        for nbr in atom.GetNeighbors():\n",
    "            jdx = nbr.GetIdx()\n",
    "            if jdx in marked:\n",
    "                marked.remove(jdx)\n",
    "                bset.add(jdx)\n",
    "    if not bset:\n",
    "        return\n",
    "    merge(mol, marked, bset)\n",
    "    aset.update(bset)\n",
    "\n",
    "# atoms connected by non-aromatic double or triple bond to any heteroatom\n",
    "# c=O should not match (see fig1, box 15).  I think using A instead of * should sort that out?\n",
    "PATT_DOUBLE_TRIPLE = Chem.MolFromSmarts('A=,#[!#6]')\n",
    "# atoms in non aromatic carbon-carbon double or triple bonds\n",
    "PATT_CC_DOUBLE_TRIPLE = Chem.MolFromSmarts('C=,#C')\n",
    "# acetal carbons, i.e. sp3 carbons connected to tow or more oxygens, nitrogens or sulfurs; these O, N or S atoms must have only single bonds\n",
    "PATT_ACETAL = Chem.MolFromSmarts('[CX4](-[O,N,S])-[O,N,S]')\n",
    "# all atoms in oxirane, aziridine and thiirane rings\n",
    "PATT_OXIRANE_ETC = Chem.MolFromSmarts('[O,N,S]1CC1')\n",
    "\n",
    "PATT_TUPLE = (PATT_DOUBLE_TRIPLE, PATT_CC_DOUBLE_TRIPLE, PATT_ACETAL, PATT_OXIRANE_ETC)\n",
    "\n",
    "def identify_functional_groups(mol):\n",
    "    marked = set()\n",
    "    # mark all heteroatoms in a molecule, including halogens\n",
    "    for atom in mol.GetAtoms():\n",
    "        if atom.GetAtomicNum() not in (6,1): # would we ever have hydrogen?\n",
    "            marked.add(atom.GetIdx())\n",
    "\n",
    "    # mark the four specific types of carbon atom\n",
    "    for patt in PATT_TUPLE:\n",
    "        for path in mol.GetSubstructMatches(patt):\n",
    "            for atomindex in path:\n",
    "                marked.add(atomindex)\n",
    "\n",
    "    # merge all connected marked atoms to a single FG\n",
    "    groups = []\n",
    "    while marked:\n",
    "        grp = set([marked.pop()])\n",
    "        merge(mol, marked, grp)\n",
    "        groups.append(grp)\n",
    "\n",
    "    # extract also connected unmarked carbon atoms\n",
    "    ifg = namedtuple('IFG', ['atomIds', 'atoms', 'type'])\n",
    "    ifgs = []\n",
    "    for g in groups:\n",
    "        uca = set()\n",
    "        for atomidx in g:\n",
    "            for n in mol.GetAtomWithIdx(atomidx).GetNeighbors():\n",
    "                if n.GetAtomicNum() == 6:\n",
    "                    uca.add(n.GetIdx())\n",
    "        ifgs.append(ifg(atomIds=tuple(list(g)), atoms=Chem.MolFragmentToSmiles(mol, g, canonical=True), type=Chem.MolFragmentToSmiles(mol, g.union(uca),canonical=True)))\n",
    "    return ifgs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_func_groups_from_smiles(smiles):\n",
    "    m = Chem.MolFromSmiles(smiles)\n",
    "    return identify_functional_groups(m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 4min 38s, sys: 2.1 s, total: 4min 40s\n",
      "Wall time: 4min 40s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "novel_df['functional_groups'] = novel_df['SMILES_generated'].apply(get_func_groups_from_smiles)\n",
    "minor_class_df['functional_groups'] = minor_class_df['SMILES_minor'].apply(get_func_groups_from_smiles)\n",
    "corrected_df['functional_groups'] = corrected_df.corrected.apply(get_func_groups_from_smiles)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "func_groups = dict()\n",
    "\n",
    "for index, row in novel_df.iterrows():\n",
    "    for ifg in row['functional_groups']:\n",
    "        if ifg.type in func_groups.keys():\n",
    "            func_groups[ifg.type] += 1\n",
    "        else:\n",
    "            func_groups[ifg.type] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "func_groups = sorted(func_groups.items(), key=lambda kv: kv[1], reverse=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "func_groups_total = 0\n",
    "\n",
    "for k, v in func_groups:\n",
    "    func_groups_total += v"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "24730\n"
     ]
    }
   ],
   "source": [
    "print(func_groups_total)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def count_to_frequency(count):\n",
    "    return count / func_groups_total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "func_groups_freq = dict((k, count_to_frequency(v)) for k, v in func_groups)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "func_groups_undersampled = dict()\n",
    "\n",
    "for index, row in minor_class_df.iterrows():\n",
    "    for ifg in row['functional_groups']:\n",
    "        if ifg.type in func_groups_undersampled.keys():\n",
    "            func_groups_undersampled[ifg.type] += 1\n",
    "        else:\n",
    "            func_groups_undersampled[ifg.type] = 1\n",
    "            \n",
    "func_groups_undersampled = sorted(func_groups_undersampled.items(), key=lambda kv: kv[1], reverse=True)\n",
    "\n",
    "func_groups_total = 0\n",
    "\n",
    "for k, v in func_groups_undersampled:\n",
    "    func_groups_total += v\n",
    "    \n",
    "func_groups_undersampled_freq = dict((k, count_to_frequency(v)) for k, v in func_groups_undersampled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "func_groups_corrected = dict()\n",
    "\n",
    "for index, row in corrected_df.iterrows():\n",
    "    for ifg in row['functional_groups']:\n",
    "        if ifg.type in func_groups_corrected.keys():\n",
    "            func_groups_corrected[ifg.type] += 1\n",
    "        else:\n",
    "            func_groups_corrected[ifg.type] = 1\n",
    "            \n",
    "func_groups_corrected = sorted(func_groups_corrected.items(), key=lambda kv: kv[1], reverse=True)\n",
    "\n",
    "func_groups_total = 0\n",
    "\n",
    "for k, v in func_groups_corrected:\n",
    "    func_groups_total += v\n",
    "    \n",
    "func_groups_corrected_freq = dict((k, count_to_frequency(v)) for k, v in func_groups_corrected)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "i = 0\n",
    "func_groups = []\n",
    "legend = []\n",
    "\n",
    "for k in func_groups_freq.keys():\n",
    "    i += 1\n",
    "    \n",
    "    func_groups.append(k)\n",
    "    legend.append('{} {}% {}% {}%'\n",
    "        .format(k,\n",
    "                round(func_groups_freq.get(k)*100, 1),\n",
    "                round(func_groups_undersampled_freq.get(k)*100, 1),\n",
    "                round(func_groups_corrected_freq.get(k)*100, 1))\n",
    "                )\n",
    "    \n",
    "    if i == 16:\n",
    "        break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "16\n",
      "16\n"
     ]
    }
   ],
   "source": [
    "print(len(func_groups))\n",
    "print(len(legend))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['cnc', 'cOC', 'cCl', 'CF', 'cO', 'cNC(C)=O', 'CN(C)C', 'csc', 'cF', 'cn(c)C', 'cSC', 'c=O', 'CC(=O)N(C)C', 'COC', 'coc', 'cnnc']\n"
     ]
    }
   ],
   "source": [
    "print(func_groups)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['cnc 12.9% 13.2% 11.9%', 'cOC 7.0% 9.4% 8.1%', 'cCl 5.4% 5.4% 5.7%', 'CF 3.9% 3.6% 3.5%', 'cO 3.3% 1.5% 2.0%', 'cNC(C)=O 3.3% 3.3% 3.0%', 'CN(C)C 2.8% 2.9% 2.8%', 'csc 2.7% 3.2% 2.8%', 'cF 2.5% 2.7% 2.6%', 'cn(c)C 2.3% 2.8% 2.2%', 'cSC 2.3% 2.5% 2.5%', 'c=O 2.3% 2.6% 1.7%', 'CC(=O)N(C)C 2.2% 2.5% 2.2%', 'COC 2.2% 2.0% 1.9%', 'coc 2.2% 2.3% 1.9%', 'cnnc 1.9% 1.9% 2.0%']\n"
     ]
    }
   ],
   "source": [
    "print(legend)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "mol_from_smarts = [Chem.MolFromSmarts(func_group) for func_group in func_groups]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "from rdkit.Chem.Draw import MolToImage, ShowMol, MolsToGridImage\n",
    "\n",
    "mols = []\n",
    "smiles_unique = []\n",
    "substructure_matches = []\n",
    "\n",
    "for substr in func_groups:\n",
    "    smarts = Chem.MolFromSmarts(substr)\n",
    "    \n",
    "    for index, row in novel_df.iterrows():\n",
    "        smiles = row['SMILES_generated']\n",
    "        mol = Chem.MolFromSmiles(smiles)\n",
    "        match = mol.GetSubstructMatch(smarts)\n",
    "        \n",
    "        if match and smiles not in smiles_unique:\n",
    "            mols.append(mol)\n",
    "            substructure_matches.append(match)\n",
    "            smiles_unique.append(smiles)\n",
    "            break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "16\n",
      "16\n",
      "16\n",
      "16\n"
     ]
    }
   ],
   "source": [
    "print(len(mols))\n",
    "print(len(smiles_unique))\n",
    "print(len(substructure_matches))\n",
    "print(len(legend))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['CCc1cc(C)nc(n1)N1CCN(CC1)C(=O)c1cnn(C)c1', 'Oc1ccc(OC)c(c1)C(=O)N1CCCC1c1cc(cc(C)n1)-c1ccc(F)cc1', 'FCNCc1cc(NC)nc(n1)C1CCCN1S(=O)(=O)c1ccccc1Cl', 'Cc1cc(nc(n1)C1CCCN1c1ccccn1)C(F)(F)F', 'COc1ccc(cc1)-c1cc(C)nc(c1)C1CCN(CC1)C(=O)c1cnn2cc(C)ccc12', 'CN(CC(=O)N1CCc2ccccc12)Cc1cc(nc(n1)C1CCCN1)CCC=C', 'CN1CCN(Cc2ccccc2)CC1C(N)=O', 'CCc1cncc(n1)C1CCCN(Cc2nccs2)C1', 'CNc1cc(CN(C)C(=O)CCCc2ccc(F)cc2)nc(n1)C1CCCC1C', 'CC(Cn1ccc(C)c1)C(=O)N1CCCCC1c1cc(cc(C)n1)-c1ccc(F)cc1', 'CC(Cc1cc(nn1C)C1CCNCC1)S(=O)(=O)c1ccc(F)c(C)c1', 'COCOC(=O)C1CCCC1c1cc(c2c(n1)n(C)c(=O)n(C)c2=O)C(F)(F)F', 'CCN(C)C(=O)C1CCCN(C1)C(=O)c1cccnc1', 'Cc1cc(cc(n1)C1CCN(CC1)C(=O)c1CCCO1)-c1ccc(F)cc1', 'Cc1nc(co1)C(=O)N1CCC(Cc2cccc3ccccc23)C1', 'CC(=C)NCCC1CCCN(C1)C(=O)c1ccn(C)n1']\n"
     ]
    }
   ],
   "source": [
    "print(smiles_unique)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "from IPython.display import SVG, display\n",
    "\n",
    "def show_svg(svg):\n",
    "    display(svg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
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       "<path d=\"M 330.24,106.954 319.569,151.458\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
       "<path d=\"M 337.54,115.764 330.07,146.917\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1;stroke-dasharray:6,6\"/>\n",
       "<path d=\"M 375.863,103.35 390.227,137.999\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
       "<path d=\"M 369.562,112.053 379.617,136.307\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1;stroke-dasharray:6,6\"/>\n",
       "<path d=\"M 386.778,151.276 365.208,169.709\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
       "<path d=\"M 377.596,147.083 362.497,159.986\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1;stroke-dasharray:6,6\"/>\n",
       "<path d=\"M 359.2,182.987 362.201,220.982\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
       "<path d=\"M 351.986,171.31 319.569,151.458\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
       "<path d=\"M 351.904,160.527 329.212,146.63\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1;stroke-dasharray:6,6\"/>\n",
       "<text style=\"font-size:15px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#000000\" x=\"39.1815\" y=\"86.7644\"><tspan>C</tspan></text>\n",
       "<text style=\"font-size:15px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#000000\" x=\"9.44918\" y=\"51.9728\"><tspan>C</tspan></text>\n",
       "<text style=\"font-size:15px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#000000\" x=\"84.178\" y=\"78.4112\"><tspan>N</tspan></text>\n",
       "<text style=\"font-size:15px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#000000\" x=\"98.6461\" y=\"156.347\"><tspan>N</tspan></text>\n",
       "<text style=\"font-size:15px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#000000\" x=\"158.907\" y=\"104.85\"><tspan>N</tspan></text>\n",
       "<text style=\"font-size:15px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#000000\" x=\"248.9\" y=\"88.1433\"><tspan>N</tspan></text>\n",
       "<text style=\"font-size:15px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#000000\" x=\"308.648\" y=\"36.6454\"><tspan>O</tspan></text>\n",
       "<text style=\"font-size:15px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#000000\" x=\"386.778\" y=\"153.254\"><tspan>N</tspan></text>\n",
       "<text style=\"font-size:15px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#000000\" x=\"351.986\" y=\"182.987\"><tspan>N</tspan></text>\n",
       "<text style=\"font-size:17px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#000000\" x=\"134.194\" y=\"243.5\"><tspan>&lt;legend here&gt;</tspan></text>\n",
       "</svg>"
      ],
      "text/plain": [
       "<IPython.core.display.SVG object>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from rdkit.Chem import rdDepictor\n",
    "from rdkit.Chem.Draw import rdMolDraw2D\n",
    "from rdkit import Chem\n",
    "from rdkit.Chem.Draw import IPythonConsole\n",
    "from IPython.display import SVG\n",
    "from rdkit.Chem.Draw.rdMolDraw2D import MolDrawOptions\n",
    "\n",
    "m = Chem.MolFromSmiles('CCc1cc(C)nc(n1)N1CCN(CC1)C(=O)c1cnn(C)c1')\n",
    "atoms = m.GetAtoms()\n",
    "\n",
    "rdDepictor.Compute2DCoords(m)\n",
    "\n",
    "matches=[4,5,6]\n",
    "\n",
    "drawer = rdMolDraw2D.MolDraw2DSVG(400,250)\n",
    "\n",
    "opts = drawer.drawOptions()\n",
    "opts.padding = 0\n",
    "opts.setHighlightColour((1, 0, 0))\n",
    "\n",
    "for match in matches:\n",
    "    opts.atomLabels[match]=atoms[match].GetSymbol()\n",
    "#     opts.atomLabels[match]=atoms[match].GetSmarts()\n",
    "\n",
    "# print(opts.getHighlightColour())\n",
    "# opts.setSymbolColour((0, 0, 0))\n",
    "# opts.useBWAtomPalette = 1\n",
    "opts.legendFontSize = 17\n",
    "opts.updateAtomPalette({7:(0,0,0), 8:(0,0,0), 9:(0,0,0), 10:(0,0,0)})\n",
    "# opts.fillHighlights = 1\n",
    "\n",
    "drawer.DrawMolecule(m, highlightAtoms=matches, \n",
    "                    highlightAtomRadii={4:0.5, 5:0.5, 6:0.5},\n",
    "                    legend='<legend here>')\n",
    "drawer.FinishDrawing()\n",
    "svg = drawer.GetDrawingText().replace('svg:','')\n",
    "SVG(svg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "svgs = []\n",
    "regular_atom_color = (0,0,0)\n",
    "highlight_color = (1, 0, 0)  # pure red\n",
    "highlight_atom_size = 0.5\n",
    "svg = None\n",
    "i = 0\n",
    "\n",
    "for smiles, matches, lgnd in zip(smiles_unique, substructure_matches, legend):\n",
    "    # molecule and atoms\n",
    "    m = Chem.MolFromSmiles(smiles)\n",
    "    atoms = m.GetAtoms()\n",
    "    # depictor\n",
    "    rdDepictor.Compute2DCoords(m)\n",
    "    # get drawer and set options\n",
    "    drawer = None\n",
    "    drawer = rdMolDraw2D.MolDraw2DSVG(400,400)\n",
    "    opts = drawer.drawOptions()\n",
    "#     opts.padding = 0\n",
    "    opts.setHighlightColour(highlight_color)\n",
    "    opts.legendFontSize = 17\n",
    "    atom_indices = []\n",
    "    for atom in atoms:\n",
    "        atom_indices.append(atom.GetIdx())\n",
    "        \n",
    "#     print('atom_indices', atom_indices)\n",
    "        \n",
    "    palette = {atom_indx:regular_atom_color for atom_indx in atom_indices}\n",
    "    \n",
    "#     print('palette', palette)\n",
    "#     print('matches', matches)\n",
    "#     print('smiles', smiles)\n",
    "#     print('legend', lgnd)\n",
    "    \n",
    "    opts.updateAtomPalette(palette)\n",
    "    # set highlighted atom labels\n",
    "    for match in matches:\n",
    "        opts.atomLabels[match]=atoms[match].GetSymbol()\n",
    "#     opts.atomLabels[match]=atoms[match].GetSmarts()\n",
    "    atom_radii_dict = {match:highlight_atom_size for match in matches}\n",
    "#     print('atom_radii_dict', atom_radii_dict)\n",
    "    # draw a molecule\n",
    "    drawer.DrawMolecule(m,\n",
    "                    highlightAtoms=matches, \n",
    "                    highlightAtomRadii=atom_radii_dict,\n",
    "#                     legend=lgnd\n",
    "                       )\n",
    "    drawer.FinishDrawing()\n",
    "    svg = drawer.GetDrawingText().replace('svg:','')\n",
    "    svgs.append(SVG(svg))\n",
    "    i += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
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       "<path d=\"M 356.886,164.817 332.321,167.046\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1;stroke-dasharray:6,6\"/>\n",
       "<path d=\"M 326.42,160.505 311.632,192.49\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
       "<path d=\"M 330.598,168.261 320.247,190.65\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1;stroke-dasharray:6,6\"/>\n",
       "<text style=\"font-size:11px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#000000\" x=\"46.7557\" y=\"209.412\"><tspan>C</tspan></text>\n",
       "<text style=\"font-size:11px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#000000\" x=\"74.9664\" y=\"230.527\"><tspan>N</tspan></text>\n",
       "<text style=\"font-size:11px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#000000\" x=\"93.3536\" y=\"176.558\"><tspan>C</tspan></text>\n",
       "<text style=\"font-size:11px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#000000\" x=\"57.7248\" y=\"176.057\"><tspan>O</tspan></text>\n",
       "<text style=\"font-size:11px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#000000\" x=\"143.561\" y=\"256.154\"><tspan>O</tspan></text>\n",
       "<text style=\"font-size:11px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#000000\" x=\"163.639\" y=\"198.382\"><tspan>N</tspan></text>\n",
       "</svg>"
      ],
      "text/plain": [
       "<IPython.core.display.SVG object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/svg+xml": [
       "<svg baseProfile=\"full\" height=\"400px\" version=\"1.1\" width=\"400px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:rdkit=\"http://www.rdkit.org/xml\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n",
       "<rect height=\"400\" style=\"opacity:1.0;fill:#FFFFFF;stroke:none\" width=\"400\" x=\"0\" y=\"0\"> </rect>\n",
       "<path d=\"M 330.777,192.947 304.8,230.285\" style=\"fill:none;fill-rule:evenodd;stroke:#FF0000;stroke-width:16px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
       "<path d=\"M 375.247,251.591 332.284,266.529\" style=\"fill:none;fill-rule:evenodd;stroke:#FF0000;stroke-width:16px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
       "<path d=\"M 332.284,266.529 304.8,230.285\" style=\"fill:none;fill-rule:evenodd;stroke:#FF0000;stroke-width:16px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
       "<ellipse cx=\"330.777\" cy=\"192.947\" rx=\"15.1621\" ry=\"15.1621\" style=\"fill:#FF0000;fill-rule:evenodd;stroke:#FF0000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
       "<ellipse cx=\"375.247\" cy=\"251.591\" rx=\"15.1621\" ry=\"15.1621\" style=\"fill:#FF0000;fill-rule:evenodd;stroke:#FF0000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
       "<ellipse cx=\"332.284\" cy=\"266.529\" rx=\"15.1621\" ry=\"15.1621\" style=\"fill:#FF0000;fill-rule:evenodd;stroke:#FF0000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
       "<ellipse cx=\"304.8\" cy=\"230.285\" rx=\"15.1621\" ry=\"15.1621\" style=\"fill:#FF0000;fill-rule:evenodd;stroke:#FF0000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
       "<path d=\"M 18.1818,253.482 47.9196,219.063\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
       "<path d=\"M 52.2159,217.569 37.2772,174.606\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
       "<path d=\"M 43.6233,220.557 28.6846,177.594\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
       "<path d=\"M 47.9196,219.063 79.4544,225.094\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
       "<path d=\"M 99.146,220.026 122.334,193.189\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
       "<path d=\"M 122.334,193.189 167.01,201.733\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
       "<path d=\"M 167.01,201.733 196.748,167.314\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
       "<path d=\"M 196.748,167.314 181.809,124.351\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
       "<path d=\"M 196.748,167.314 241.425,175.858\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
       "<path d=\"M 181.809,124.351 211.547,89.932\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
       "<path d=\"M 211.547,89.932 256.224,98.4762\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
       "<path d=\"M 256.224,98.4762 268.526,133.858\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
       "<path d=\"M 264.612,149.02 241.425,175.858\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
       "<path d=\"M 277.733,142.696 315.839,149.984\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
       "<path d=\"M 319.281,152.957 342.468,126.12\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
       "<path d=\"M 312.397,147.01 335.585,120.172\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
       "<path d=\"M 315.839,149.984 328.141,185.366\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
       "<path d=\"M 337.348,194.934 374.316,206.114\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
       "<path d=\"M 340.26,205.319 366.137,213.145\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1;stroke-dasharray:6,6\"/>\n",
       "<path d=\"M 325.503,200.528 310.075,222.704\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
       "<path d=\"M 330.657,209.05 319.857,224.573\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1;stroke-dasharray:6,6\"/>\n",
       "<path d=\"M 374.316,206.114 375.092,244.01\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
       "<path d=\"M 365.337,211.985 365.88,238.512\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1;stroke-dasharray:6,6\"/>\n",
       "<path d=\"M 368.677,253.875 338.855,264.245\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
       "<path d=\"M 361.216,246.838 340.341,254.097\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1;stroke-dasharray:6,6\"/>\n",
       "<path d=\"M 329.992,274.11 319.117,310.068\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
       "<path d=\"M 326.536,258.948 310.549,237.866\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
       "<path d=\"M 331.386,250.289 320.196,235.532\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1;stroke-dasharray:6,6\"/>\n",
       "<text style=\"font-size:15px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#000000\" x=\"79.4544\" y=\"235.188\"><tspan>NH</tspan></text>\n",
       "<text style=\"font-size:15px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#000000\" x=\"264.591\" y=\"149.02\"><tspan>N</tspan></text>\n",
       "<text style=\"font-size:15px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#000000\" x=\"338.496\" y=\"123.146\"><tspan>O</tspan></text>\n",
       "<text style=\"font-size:15px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#000000\" x=\"324.207\" y=\"200.528\"><tspan>C</tspan></text>\n",
       "<text style=\"font-size:15px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#000000\" x=\"368.677\" y=\"259.172\"><tspan>C</tspan></text>\n",
       "<text style=\"font-size:15px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#000000\" x=\"325.713\" y=\"274.11\"><tspan>N</tspan></text>\n",
       "<text style=\"font-size:15px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#000000\" x=\"298.23\" y=\"237.866\"><tspan>N</tspan></text>\n",
       "</svg>"
      ],
      "text/plain": [
       "<IPython.core.display.SVG object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for svg in svgs:\n",
    "    show_svg(svg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "16\n"
     ]
    }
   ],
   "source": [
    "print(len(svgs))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "<svg baseProfile=\"full\" height=\"250px\" version=\"1.1\" width=\"400px\" xml:space=\"preserve\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:rdkit=\"http://www.rdkit.org/xml\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n",
       "<rect height=\"250\" style=\"opacity:1.0;fill:#FFFFFF;stroke:none\" width=\"400\" x=\"0\" y=\"0\"> </rect>\n",
       "<path d=\"M 72.9643,154.157 113.87,146.564\" style=\"fill:none;fill-rule:evenodd;stroke:#FF0000;stroke-width:16px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
       "<path d=\"M 127.747,107.341 113.87,146.564\" style=\"fill:none;fill-rule:evenodd;stroke:#FF0000;stroke-width:16px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
       "<ellipse cx=\"72.9643\" cy=\"154.157\" rx=\"13.8683\" ry=\"13.8683\" style=\"fill:#FF0000;fill-rule:evenodd;stroke:#FF0000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
       "<ellipse cx=\"127.747\" cy=\"107.341\" rx=\"13.8683\" ry=\"13.8683\" style=\"fill:#FF0000;fill-rule:evenodd;stroke:#FF0000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
       "<ellipse cx=\"113.87\" cy=\"146.564\" rx=\"13.8683\" ry=\"13.8683\" style=\"fill:#FF0000;fill-rule:evenodd;stroke:#FF0000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
       "<path d=\"M 18.1818,200.974 59.0877,193.38\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
       "<path d=\"M 59.0877,193.38 70.5111,161.091\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
       "<path d=\"M 67.0385,147.223 45.9349,122.529\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
       "<path d=\"M 70.1987,138.113 55.4262,120.827\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1;stroke-dasharray:6,6\"/>\n",
       "<path d=\"M 78.9744,153.042 107.86,147.679\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
       "<path d=\"M 81.7885,144.056 102.008,140.302\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1;stroke-dasharray:6,6\"/>\n",
       "<path d=\"M 45.9349,122.529 59.8115,83.3062\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
       "<path d=\"M 55.8609,119.421 65.5745,91.9649\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1;stroke-dasharray:6,6\"/>\n",
       "<path d=\"M 59.8115,83.3062 32.7821,51.6775\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
       "<path d=\"M 59.8115,83.3062 94.7073,76.8281\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
       "<path d=\"M 66.5646,90.5157 90.9917,85.981\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1;stroke-dasharray:6,6\"/>\n",
       "<path d=\"M 106.643,82.6466 121.821,100.407\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
       "<path d=\"M 102.594,90.7165 113.219,103.149\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1;stroke-dasharray:6,6\"/>\n",
       "<path d=\"M 125.294,114.275 116.323,139.629\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
       "<path d=\"M 116.104,115.303 109.824,133.051\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1;stroke-dasharray:6,6\"/>\n",
       "<path d=\"M 133.757,106.225 162.643,100.863\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
       "<path d=\"M 174.578,106.681 195.682,131.376\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
       "<path d=\"M 171.106,92.8132 182.529,60.5249\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
       "<path d=\"M 195.682,131.376 236.588,123.782\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
       "<path d=\"M 236.588,123.782 248.011,91.4939\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
       "<path d=\"M 244.539,77.6256 223.435,52.9311\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
       "<path d=\"M 256.475,83.444 291.37,76.966\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
       "<path d=\"M 223.435,52.9311 182.529,60.5249\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
       "<path d=\"M 295.293,78.3536 306.716,46.0653\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
       "<path d=\"M 287.448,75.5783 298.872,43.29\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
       "<path d=\"M 291.37,76.966 318.4,108.595\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
       "<path d=\"M 318.4,108.595 359.875,105.318\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
       "<path d=\"M 325.276,116.398 354.309,114.105\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1;stroke-dasharray:6,6\"/>\n",
       "<path d=\"M 318.4,108.595 308.699,149.053\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
       "<path d=\"M 325.036,116.604 318.246,144.924\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1;stroke-dasharray:6,6\"/>\n",
       "<path d=\"M 359.875,105.318 372.933,136.817\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
       "<path d=\"M 354.147,113.23 363.288,135.279\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1;stroke-dasharray:6,6\"/>\n",
       "<path d=\"M 369.798,148.888 350.19,165.645\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
       "<path d=\"M 361.451,145.075 347.725,156.805\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1;stroke-dasharray:6,6\"/>\n",
       "<path d=\"M 344.727,177.715 347.456,212.256\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
       "<path d=\"M 338.169,167.1 308.699,149.053\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
       "<path d=\"M 338.094,157.297 317.465,144.664\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1;stroke-dasharray:6,6\"/>\n",
       "<text style=\"font-size:13px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#000000\" x=\"66.9542\" y=\"161.091\"><tspan>C</tspan></text>\n",
       "<text style=\"font-size:13px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#000000\" x=\"94.7073\" y=\"82.6466\"><tspan>N</tspan></text>\n",
       "<text style=\"font-size:13px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#000000\" x=\"121.737\" y=\"114.275\"><tspan>C</tspan></text>\n",
       "<text style=\"font-size:13px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#000000\" x=\"107.86\" y=\"153.498\"><tspan>N</tspan></text>\n",
       "<text style=\"font-size:13px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#000000\" x=\"162.643\" y=\"106.681\"><tspan>N</tspan></text>\n",
       "<text style=\"font-size:13px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#000000\" x=\"244.454\" y=\"91.4939\"><tspan>N</tspan></text>\n",
       "<text style=\"font-size:13px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#000000\" x=\"298.771\" y=\"44.6777\"><tspan>O</tspan></text>\n",
       "<text style=\"font-size:13px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#000000\" x=\"369.798\" y=\"150.686\"><tspan>N</tspan></text>\n",
       "<text style=\"font-size:13px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#000000\" x=\"338.169\" y=\"177.715\"><tspan>N</tspan></text>\n",
       "<text style=\"font-size:17px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#000000\" x=\"88.8265\" y=\"243.5\"><tspan>cnc 12.9% 13.2% 11.9%</tspan></text>\n",
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     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svgs[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
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       "<path d=\"M 289.204,95.6528 320.801,89.0384\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
       "<path d=\"M 295.267,100.98 317.385,96.3499\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1;stroke-dasharray:6,6\"/>\n",
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       "<path d=\"M 342.328,113.095 369.979,107.307\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
       "<path d=\"M 342.328,113.095 332.258,143.766\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
       "<path d=\"M 334.683,115.682 327.634,137.151\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1;stroke-dasharray:6,6\"/>\n",
       "<path d=\"M 332.258,143.766 300.661,150.381\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
       "<path d=\"M 326.195,138.439 304.077,143.069\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1;stroke-dasharray:6,6\"/>\n",
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       "<text style=\"font-size:10px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#000000\" x=\"71.3712\" y=\"155.81\"><tspan>C</tspan></text>\n",
       "<text style=\"font-size:10px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#000000\" x=\"87.0642\" y=\"183.817\"><tspan>O</tspan></text>\n",
       "<text style=\"font-size:10px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#000000\" x=\"71.1988\" y=\"211.724\"><tspan>C</tspan></text>\n",
       "<text style=\"font-size:10px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#000000\" x=\"135.573\" y=\"156.009\"><tspan>O</tspan></text>\n",
       "<text style=\"font-size:10px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#000000\" x=\"136.107\" y=\"100.095\"><tspan>N</tspan></text>\n",
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       "<text style=\"font-size:17px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#000000\" x=\"101.316\" y=\"243.5\"><tspan>cOC 7.0% 9.4% 8.1%</tspan></text>\n",
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     "execution_count": 32,
     "metadata": {},
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   "source": [
    "svgs[1]"
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  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
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\n",
      "text/plain": [
       "<PIL.PngImagePlugin.PngImageFile image mode=RGB size=800x800 at 0x7FC3EC475198>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from rdkit.Chem.Draw.rdMolDraw2D import MolDrawOptions\n",
    "\n",
    "regular_atom_color = (0,0,0)\n",
    "highlight_color = (1, 0, 0)  # pure red\n",
    "highlight_atom_size = 0.5\n",
    "\n",
    "opts = MolDrawOptions()\n",
    "\n",
    "\n",
    "\n",
    "# rdMolDraw2D.MolDraw2D.setDrawOptions(opts)\n",
    "\n",
    "img = MolsToGridImage(mols, \n",
    "                molsPerRow=4, \n",
    "                legends=legend, \n",
    "                highlightAtomLists=substructure_matches,\n",
    "#                 highlightBondColors=[(1,0,0)],\n",
    "               )\n",
    "img"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
